X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible Decision Tree in Artificial Neural Network

David C. Wyld et al. (Eds) : CSEN, AISO, NCWC, SIPR - 2015
pp. 37–54, 2015. © CS & IT-CSCP 2015 DOI : 10.5121/csit.2015.51405
X-TREPAN: A MULTI CLASS REGRESSION
AND ADAPTED EXTRACTION OF
COMPREHENSIBLE DECISION TREE IN
ARTIFICIAL NEURAL NETWORKS
Awudu Karim and Shangbo Zhou
College of Computer Science, Chongqing University,
Chongqing, 400030, China
awudubody@yahoo.com
ABSTRACT
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
KEYWORDS
Neural Network, Feed Forward, Decision Tree, Extraction, Classification, Comprehensibility.
1. INTRODUCTION
Artificial neural networks are modeled based on the human brain architecture. They offer a
means of efficiently modeling large and complex problems in which there are hundreds of
independent variables that have many interactions. Neural networks generate their own implicit
rules by learning from examples. Artificial neural networks have been applied to a variety of
problem domains [1] such as medical diagnostics [2], games [3], robotics [4], speech generation
[5] and speech recognition [6]. The generalization ability of neural networks has proved to be
superior to other learning systems over a wide range of applications [7].
However despite their relative success, the further adoption of neural networks in some areas has
been impeded due to their inability to explain, in a comprehensible form, how a decision has been
arrived at. This lack of transparency in the neural network’s reasoning has been termed the Black
Box problem. Andrews et al. [8] observed that ANNs must obtain the capability to explain their
decision in a human-comprehensible form before they can gain widespread acceptance and to

38 Computer Science & Information Technology (CS & IT)
enhance their overall utility as learning and generalization tools. This work intends to enhance
TREPAN to be able to handle not only multi-class classification type but also multi-class
regression type problems. And also to demonstrate that X-TREPAN can understand and analyze
generalized feed forward networks (GFF). TREPAN is tested on different datasets and best
settings for TREPAN algorithm are explored based on database type to generate heuristics for
various problem domains. The best TREPAN model is then compared to the baseline C4.5
decision tree algorithm to test for accuracy.
Neural networks store their “Knowledge” in a series of real-valued weight matrices representing
a combination of nonlinear transforms from an input space to an output space. Rule extraction
attempts to translate this numerically stored knowledge into a symbolic form that can be readily
comprehended. The ability to extract symbolic knowledge has many potential advantages: the
knowledge obtained from the neural network can lead to new insights into patterns and
dependencies within the data; from symbolic knowledge, it is easier to see which features of the
data are the most important; and the explanation of a decision is essential for many applications,
such as safety critical systems. Andrews et al. and Ticke et al. [9], [10] summarize several
proposed approaches to rule extraction. Many of the earlier approaches required a specialized
neural network architectures or training schemes. This limited their applicability; in particular
they cannot be applied to in situ neural networks. The other approach is to view the extraction
process as learning task. This approach does not examine the weight matrices directly but tries to
approximate the neural network by learning its input-output mappings. Decision trees are a
graphical representation of a decision process. The combination of symbolic information and
graphical presentation make decision trees one of the most comprehensible representations of
pattern recognition knowledge.
2. BACKGROUND AND LITERATURE REVIEW
2.1 Artificial Neural Network
Artificial neural networks as the name implies are modeled on the architecture of the human
brain. They offer a means of efficiently modeling large and complex problems in which there
may be hundreds of independent variables that have many interactions. Neural networks learn
from examples by generating their own implicit rules. The generalization ability of neural
networks has proved to be equal or superior to other learning systems over a wide range of
applications.
2.2 Neural Network Architecture
A neural network consists of a large number of units called processing elements or nodes or
neurons that are connected on a parallel scale. The network starts with an input layer, where each
node corresponds to an independent variable. Input nodes are connected to a number of nodes in
a hidden layer. There may be more than one hidden layer and an output layer. Each node in the
hidden layer takes in a set of inputs (X1, X2, …, Xm), multiplies them by a connection weight
(W1, W2, …, Wm), then applies a function, f(WTX) to them and then passes the output to the
nodes of the next layer. The connection weights are the unknown parameters that are estimated
by an iterative training method to indicate the connection’s strength and excitation. The
calculation of the final outputs of the network proceeds layer by layer [11]. Each processing
element of the hidden layer computes its output as a function of linear combination of inputs

Computer Science & Information Technology (CS & IT) 39
from the previous layer plus a bias. This output is propagated as input to the next layer and so on
until the final layer is reached. Figure 1 shows the model of a single neuron [12]
Figure 1. Model of a Single Neuron
The output of the neuron can be expressed as
In the above equations, W is the weight vector of the neural node, defined as
[ ]T
mwwwwW .......,.........,, 321=
and X is the input vector , defined as
[ ]T
mxxxxX .......,.........,, 321=
Figure 2. Shows a typical neural network architecture representation.
Figure 2. Neural Network Architecture

These are different types of activation functions that can be applied at the node of the network.
Two of the most commonly used neural network functions are the hyperbolic and logistic (or
sigmoid) functions. They are sometimes referred to as “squashing” functions since they map the
inputs into a bounded range. Table 1 shows a list of activation functions that are available for use
in neural networks.
Table 1. Activation Functions used in Neural Networks Adapted from [13]
Functions Definition Range
Identity x ( )+∞∞− ,
Logistic
( )x
e−
−1
1 ( )1,0 +
Hyperbolic
Exponential
xx
xx
ee
ee
−
−
+
−
x
e
( )1,1 +−
( )+∞,0
Softmax
∑
−
i
x
x
i
e
e ( )1,0 +
Unit Sum
Square root ∑i
ix
x
x
( )1,0 +
( )+∞,0
Sine
Ramp
Step
( )xSin





+≥+
<−
−≤−
1,1
1,
1,1
x
xx
x



≥+
<
0,1
0,0
x
x
( )1,0 +
( )1,1 +−
( )1,0 +
2.3 Multilayer Perceptrons
Multilayer Perceptrons (MLOs) are layered feed forward networks typically trained with back
propagation. These networks have been used in numerous applications. Their main advantage is
that they are easy to use, and that they can approximate any input/output map. A major
disadvantage is that they train slowly, require lots of training data (typically three times more
training samples then network weights)[14].

Figure 3. A schematic Multilayered Perceptron Network
A Generalized Feed Forward (GFF) network is a special case of a Multilayer Perception wherein
connections can jump over one or more layers. Although an MLP can solve any problem that a
GFF can solve, in practice, a GFF network can solve the problem more efficiently [14]. Figure 4
shows a general schematic of a Generalized Feed Forward Network.
Figure 4. Generalized Feed Forward Networks

2.4 Neural Networks for Classification and Regression
Neural networks are one of the most widely used algorithms for classification problems. The
output layer is indicative of the decision of the classifier. The cross entropy error function is most
commonly used in classification problems in combination with logistic or soft max activation
functions. Cross entropy assumes that the probability of the predicated values in a classification
problem lie between 0 and 1. In a classification problem each output node of a neural network
represents a different hypothesis and the node activations represent the probability that each
hypothesis may be true. Each output node represents a probability distribution and the cross
entropy measures calculate the difference between the network distribution and the actual
distribution [15]. Assigning credit risk (good or bad) is an example of a neural network
classification problem. Regression involves prediction the values of a continuous variable based
on previously collected data. Mean square error is the function used for computing the error in
regression networks. Projecting the profit of a company based on previous year’s data is
regression type neural network problem.
2.5 Neural Network Training
The neural network approach is a two stage process. In the first stage a generalized network that
maps the inputs data to the desired output using a training algorithm is derived. The next stage is
the “production” phase where the network is tested for its generalization ability against a new set
of data.
Often the neural network tends to over train and memorizes the data. To avoid this possibility, a
cross-validation data set is use. The cross validation data set is a part of the data set which is set
aside before training and is used to determine the level of generalization produced by the training
set. As training processes the training error drops progressively. At first the cross validation error
decreases but then begins to rise as the network over trains. Best generalization ability of the
network can be tapped by stopping the algorithm where the error on the cross validation set starts
to rise. Figure 5 illustrates the use of cross-validation during training.
Figure 5. Use of cross-Validation during Training

2.6 Rule Extraction from Neural Networks
Although neural networks are known to be robust classifiers, they have found limited use in
decision-critical applications such as medical systems. Trained neural networks act like black
boxes and are often difficult to interpret [16]. The availability of a system that would provide an
explanation of the input/output mappings of a neural network in the form of rules would thus be
very useful. Rule extraction is one such system that tries to elucidate to the user, how the neural
network arrived at its decision in the form of if-then rules.
Two explicit approaches have been defined to date for transforming the knowledge and weights
contained in a neural network into a set of symbolic rules de-compositional and pedagogical [17].
In the de-compositional approach the focus is on the extracting rules at an individual hidden
and/or output level into a binary outcome. It involves the analysis of the weight vectors and
biases associated with the processing elements in general. The subset [18] algorithm is an
example of this category. The pedagogical approach treats neural networks like black boxes and
aims to extract rules that map inputs directly to its output. The Validity Interval Analysis (VIA)
[19] proposed by Thrum and TREPAN [20] is an example of one such technique .Andrews et al
[21] proposed a third category called eclectic which combines the elements of the basic
categories.
2.7 Decision Trees
A decision tree is a special type of graph drawn in the form of a tree structure. It consists of
internal nodes each associated with a logical test and its possible consequences. Decision trees
are probably the most widely used symbolic learning algorithms as are neural networks in the
non-symbolic category.
2.8 Decision Tree Classification
Decision trees classify data through recursive partitioning of the data set into mutually exclusive
subsets which best explain the variation in the dependent variable under observation[22][23] .
Decision trees classify instances (data points) by sorting them down the tree from the root node to
some leaf node. This lead node gives the classification of the instance. Each branch of the
decision tree represents a possible scenario of decision and its outcome.
Decision tree algorithms depict concept descriptions in the form of a tree structure. They begin
learning with a set of instances and create a tree structure that is used to classify new instances.
An instance in a dataset is described by a set of feature values called attributes, which can have
either continuous or nominal values. Decision tree induction is best suitable for data where each
example in the dataset is described by a fixed number of attributes for all examples of that
dataset. Decision tree methods use a divide and conquer approach. They can be used to classify
an example by starting at the root of the tree and moving through it until a leaf node is reached,
which provides the classification of the instance.
Each node of a decision tree specifies a test of some attribute and each branch that descends from
the node corresponds to a possible value for this attribute. The following example illustrates a
simple decision tree.

3. TREPAN ALGORITHM
The TREPAN [24] and [25] algorithms developed by Craven et al are novel rule-extraction
algorithms that mimic the behavior of a neural network. Given a trained Neural Network,
TREPAN extracts decision trees that provide a close approximation to the function represented
by the network. In this work, we are concerned with its application to trained variety of learned
models as well. TREPAN uses a concept of recursive partitioning similar to other decision tree
induction algorithms. In contrast to the depth-first growth used by other decision tree algorithms,
TREPAN expands using the best first principle. Thus node which increases the fidelity of the
fidelity of the tree when expanded is deemed the best.
In conventional decision tree induction algorithms the amount of training data decreases as one
traverses down the tree by selecting splitting tests. Thus there is not enough data at the bottom of
the tree to determine class labels and is hence poorly chosen. In contrast TREPAN uses an
‘Oracle’ to answer queries, in addition to the training samples during the inductive learning
process. Since the target here is the function represented by the neural network, the network itself
is used as the ‘Oracle’. This learning from larger samples can prevent the lack of examples for the
splitting tests at lower levels of the tree, which is usually a problem with conventional decision
tree learning algorithms. It ensures that there is a minimum sample of instances available at a
node before choosing a splitting test for that node where minimum sample is one of the user
specified parameters. If the number of instances at the node, say m is less than minimum sample
then TREPAN will make membership queries equal to (minimum sample m) from the ‘Oracle’
and then make a decision at the node. The following illustrates a pseudocode of the TREPAN
algorithm [26].

Algorithm : TREPAN
Input: Trained neural network; training examples {ܺ௜ܻ௜} = where yi , is the class label predicted
by the trained neural network on the training example Xi , global stopping criteria.
Output : extracted decision tree
Begin
Initialize the tree as a leaf node
While global stopping criteria are not met and the current tree can be further refined
Do
Pick the most promising leaf node to expand
Draw sample of examples
Use the trained network to label these examples
Select a splitting test for the node
For each possible outcome of the test make a new leaf node
End
End
3.1 M-of-N Splitting tests
TREPAN uses the m-of-n test to partition the part of the instance space covered by a particular
internal node. An m-of-n expression (a Boolean expression) is fulfilled when at least an integer
threshold m of its n literals hold true. For example, consider four features a, b, c and d; the m-of-n
test: 3-of-{a, b > 3.3, c, d} at a node signifies that if any of the 3 conditions of the given set of 4
are satisfied then an example will pass through that node. TREPAN employs a beam search
method with beam width as a user defined parameter to find the best m-of-n test. Beam search is
heuristic best-first each algorithm that evaluates that first n node (where n is a fixed value called
the ‘beam width’) at each tree depth and picks the best out of them for the split. TREPAN uses
both local and global stopping criteria. The growth of the tree stops when any of the following
criteria are met: the size of the tree which is a user specific parameter or when all the training
examples at node fall in the same class.
3.2 Single Test TREPAN and Disjunctive TREPAN
In addition to TREPAN algorithm, Craven has also developed two of its important variations.
The single test TREPAN algorithm is similar to TREPAN in all respects except that as its name
suggests it uses single feature tests at the internal nodes. Disjunctive TREPAN on the other hand,

uses disjunctive “OR” tests at the internal nodes of the tree instead of the m-of-n tests. A more
detailed explanation of the TREPAN algorithm can be found in Craven’s dissertation [27].
Baesens et al [28] have applied TREPAN to credit risk evaluation and reported that it yields very
good classification accuracy as compared to the logistic regression classifier and the popular C4.5
algorithm.
4. C4.5 ALGORITHM
The C4.5 algorithm [29] is one of the most widely used decision tree learning algorithms. It is an
advanced and incremental software extension of the basic ID3 algorithm [30] designed to address
the issues that were not dealt with by ID3. The C4.5 algorithm has its origins in Hunt’s Concept
Learning Systems (CLS) [31]. It is a non-incremental algorithm, which means that it derives its
classes from an initial set of training instances. The classes derived from these instances are
expected to work for all future test instances. The algorithm uses the greedy search approach to
select the best attribute and never looks back to reconsider earlier choices. The C4.5 algorithm
searches through the attributes of the training instances and finds the attribute that best separates
the data. If this attribute perfectly classifies the training set then it stops else it recursively works
on the remaining in the subsets (m = the remaining possible values of the attribute) to get their
best attribute. Some attributes split the data more purely than others. Their values correspond
more consistently with instances that have particular values of the target class. Therefore it can be
said that they contain more information than the other attributes. But there should be a method
that helps quantify this information and compares different attributes in the data which will
enable us to decide which attribute should be placed at the highest node in the tree.
4.1 Information Gain, Entropy Measure and Gain Ratio
A fundamental part of any algorithm that constructs a decision tree from a dataset is the method
in which it selects attributes at each node of the tree for splitting so that the depth of the tree is the
minimum. ID3 uses the concept of Information Gain which is based on Information theory [32]
to select the best attributes. Gain measures how well a given attribute separates training examples
into its target classes. The one with the highest information is selected. Information gain
calculates the reduction in entropy (or gain information) that would result from splitting the data
into subsets based on an attribute.
The information gain of example set S on attribute A is defined as,
( ) ( ) ( )∑−= v
v
SEntropy
S
S
SEntropyASGain ,
In the above equation, S is the number of instances and |Sv| is a subset of instances of S where A
takes the value v. Entropy is a measure of the amount of information in an attribute. The higher
the entropy, the more the information is required to completely describe the data. Hence, when
building the decision tree, the idea is to decrease the entropy of the dataset until we reach a subset
that is pure (a leaf), that has zero entropy and represents instances that all belong to one class.
Entropy is given by,
( ) ( ) ( )∑−= IpIpSEntropy 2log
Eq.1
Eq.2

where p(I) is the proportion of S belonging to Class I.
Suppose we are constructing a decision tree with ID3 that will enable us to decide if the weather
is favorable to play football. The input data to ID3 is shown in table 2 below adapted from
Quinlan’s C4.5.
Table 2. Play Tennis Examples Dataset
Day Outlook Temperature Humidity Wind Play Tennis
1 Sunny Hot High Weak No
2 Sunny Hot High Strong No
3 Overcast Hot High Weak Yes
4 Rain Mild High Weak Yes
5 Rain Cool Normal Weak Yes
6 Rain Cool Normal Strong No
7 Overcast Cool Normal Strong Yes
8 Sunny Mild High Weak No
9 Sunny Cool Normal Weak Yes
10 Rain Mild Normal Weak Yes
11 Sunny Mild Normal Strong Yes
12 Overcast Mild High Strong Yes
13 Overcast Hot Normal Weak Yes
14 Rain Mild High Strong No
In this example,
( ) 9450.0
14
5
log
14
5
14
9
log
14
9
22 =











−











−=SEntropy
(Note: Number of instances where play tennis = yes is 9 and play tennis = No is 5)
The best attribute of the four is selected by calculating the Information Gain for each attribute as
follows,
( ) ( ) ( ) ( ) ( )RainEntrOvercastEntrSunnyEntrSEntrOutlookSGain .
14
5
.
14
4
.
14
5
., −−−=
( ) 2670.03364.003364.09450.0, =−−−=OutlookSGain
Similarly, ( ) 42.0, −=TempSGain and ( ) 1515.0, =WindSGain
The attribute outlook has the highest gain and hence it is used as the decision attribute in the root
node. The root node has three branches since the attribute outlook has three possible values,
(Sunny, Overcast, and Rain). Only the remaining attributes are tested at the sunny branch node
since outlook has already been used at the node. This process is recursively repeated until: all the
training instances have been classified or every attribute has been utilized in the decision tree.
The ID3 has a strong bias in favor of tests with many outcomes. Consider an employee database
that consists of an employee identification number. Every attribute intended to be unique and
Eq.3
Eq.4

partitioning any set of training cases on the values of this attribute will lead to a large number of
subsets, each containing only one case. Hence the C4.5 algorithm incorporates use of a statistic
called the “Gain Ratio” that compensates for the number of attributes by normalizing with
information encoded in the split itself.
( )
( )AI
ASGain
GainRatio
,
=
In the above equation,
( ) ( ) ( )∑−= AA IpIpAI 2log
C4.5 has another advantage over ID3; it can deal with numeric attributes, missing values and
noisy data.
5. EXPERIMENTATION AND RESULT ANALYSIS
We analyze three datasets with classes greater than two and we compare the results of Single-test
TREPAN and C4.5 with that of X-TREPAN in terms of comprehensibility and classification
accuracy. A generalized feed forward network was trained in order to investigate the ability of X-
TREPAN in comprehending GFF networks. The traditional ‘using-network’ command was used
to validate that X-TREPAN was producing correct outputs for the network. In all the
experiments, we adopted the Single-test TREPAN as the best variant for comparison with the
new model.
5.1 Body Fat
Body Fat is a regression problem in the simple machine learning dataset category. The instances
are sought to predict body fat percentage based on body characteristics. A 14-4-1 MLP with
hyperbolic tangent function was used to train the network for 1500 epochs giving an r
(correlation co-efficient) value of 0.9882. Figure 6 shows the comparison of classification
accuracy of body fat by the three models.
Figure 6. Comparison of classification accuracy of Body fat by the three algorithms
Eq.5
Eq.6

TREPAN achieves a classification accuracy of 94% and C4.5 produces a classification accuracy
of 91% while X-TREPAN achieves a comparatively much higher accuracy of 96%. Additionally,
both X-TREPAN and TREPAN generate similar trees in terms of size but accuracy and
comprehensibility attained by X-TREPAN are comparatively higher.
The tables below show the confusion matrix of the classification accuracy achieved by TREPAN
in comparison with X-TREPAN. While TREPAN produces a classification accuracy of 92.06%
X-TREPAN produces a comparatively much higher accuracy of 96.83% as indicated in Table 3
below.
Table 3. Body Fat Confusion Matrix (X-TREPAN)
Actual/Predicted Toned Healthy Flabby Obese
Toned 13 0 0 0
Healthy 1 21 0 0
Flabby 0 0 9 1
Obese 0 0 0 18
Classification Accuracy
(%)
92.86% 100.00% 100.00% 94.74%
Total Accuracy (%) 96.83%
Table 4. Body Fat Confusion Matrix (TREPAN)
Actual/Predicted Toned Healthy Flabby Obese
Toned 13 0 0 0
Healthy 1 20 0 0
Flabby 0 0 9 0
Obese 0 0 3 16
(%)
92.86% 95.24% 75.00% 100.00%
Additionally, both TREPAN and X-TREPAN generate identical trees in terms of size but
accuracy attained by X-TREPAN is comparatively higher.
5.2 Outages
Outages constitute a database from the small dataset category. A12-3-1 MLP network with a
hyperbolic tangent and bias axon transfer functions in the first and the second hidden layer
respectively gave the best accuracy. The model was trained for 12000 epochs and achieved an r
(correlation co-efficient) value of 0.985 (or an r2 of (0.985)2). Figure 7 shows the comparison of
classification accuracy of outages by the three algorithms.

Figure 7. Comparison of classification accuracy of Outages by the three algorithms
In terms of classification accuracy, as can be seen in the figure above, TREPAN achieves 84%,
C4.5 achieves 91% while X-TREPAN achieves 92%.
However, here TREPAN, C4.5 and X-TREPAN all generate very different trees in terms of size
with C4.5 producing the largest and most complex decision tree while X-TREPAN produces the
simplest and smallest decision tree with comparatively higher accuracy and comprehensibility.
The tables below show the confusion matrix of the classification accuracy achieved by both
algorithms. X-TREPAN achieves 85% while TREPAN achieves 76%.
Table 5. Outages Confusion Matrix (X-TREPAN)
Actual/Predicted C11 C12 C13 C14 C15
C11 3 0 0 0 0
C12 4 48 5 0 0
C13 0 1 8 0 0
C14 0 0 1 5 0
C15 0 0 0 0 0
(%)
42.86% 97.96% 57.14% 100% 0.00%
Table 6. Outages Confusion Matrix (TREPAN)
Actual/Predicted C11 C12 C13 C14 C15
C11 2 5 0 0 0
C12 3 43 3 0 0
C13 0 6 7 1 0
C14 0 0 0 5 0
C15 0 0 0 0 0
(%)
40.00% 79.63% 70.00% 83.33% 0.00%

5.3 Admissions
A typical University admissions database model based on a 22-15-10-2 MLP network. Two
hidden layers with the hyperbolic tangent transfer functions were used for modeling. The best
model was obtained by the X-TREPAN with a minimum sample size of 1000, tree size of 50 and
classification accuracy of 74%. Figure 8 gives the comparison of classification accuracy of
Admissions by the three models.
Figure 8. Comparison of classification accuracy of Admissions by the three algorithms
On the other hand, C4.5 achieved an accuracy of 71.97% (not rounded) almost equaling that of
TREPAN of 72%, but produced a significantly large and complex decision tree.
In terms of Confusion Matrix, TREPAN achieved an accuracy of 71.6% very close to that of X-
TREPAN. The confusion matrix is shown in the Tables below.
Table 7. Admissions Confusion Matrix (X-TREPAN)
Actural/Predicted Yes No
Yes 401 279
No 168 754
Classification Accuracy (%) 70.47% 72.99
Table 8. Admissions Confusion Matrix (TREPAN)
Actural/Predicted Yes No
Yes 379 190
No 259 774
Classification Accuracy (%) 59.40% 80.29

6. PERFORMANCE ASSESSMENT
6.1 Classification Accuracy
The classification accuracy or error rate is the percentage of correct predictions made by the
model over a data set. It is assessed using the confusion matrix. A confusion matrix is a matrix
plot of predicted versus actual classes with all of the correct classifications depicted along the
diagonal of the matrix. It gives the number of correctly classified instances, incorrectly classified
instances and overall classification accuracy.
The accuracy of the classifier is given by the formula,
( ) ( ) 100
)(
% ×
+++
+
=
TNFPFNTP
TNTP
Accuracy
Where true positive = (TP), true negative = (TN) false positive = (FP) and false negative = (FN).
A false positive (FP) is when a negative instance incorrectly classified as a positive and false
negative (FN) is when a positive instance is incorrectly classified as a negative. A true positive
(TP) is when an instance is correctly classified as positive and true negative (TN) is when an
instance is correctly classified as negative and so on.
A confusion matrix is a primary tool in visualizing the performance of a classifier. However it
does not take into account the fact that some misclassifications are worse than others. To
overcome this problem we use a measure called the Kappa Statistic which considers the fact that
correct values in a confusion matrix are due to chance agreement.
The Kappa statistic is defined as,
( )
)(1
)()(^
EP
EPAP
k
−
−
=
In this equation, P(A) is the proportion of times the model values were equal to the actual value
and, P(E) is the expected proportion by Chance.
For perfect agreement, Kappa = 1. For example: a Kappa statistic of 0.84 would imply that the
classification process was avoiding 84% of the errors that a completely random classification
would generate.
6.2 Comprehensibility
The comprehensibility of the tree structure decreases with the increase in the size and complexity.
The principle of Occam’s Razors says “when you have two competing theories which make
exactly the same projections, the one that is simpler is the better” [33]. Therefore, among the
three algorithms, X-TREPAN is better as it produces smaller and simpler trees as against Single-
test TREPAN and C4.5 in most scenarios.
Eq.5
Eq.6

7. CONCLUSION
The TREPAN algorithm code was modified (X-TREPAN) to be able to work with multi-class
regression type problems. Various experiments were run to investigate its compatibility with
generalized feed forward networks. The weights and network file were restructured to present
GFF networks in a format recognized by X-TREPAN. Neural Network models were trained on
each dataset varying parameters like network architecture and transfer functions. The weights and
biases obtained from the trained models of the three datasets were fed to X-TREPAN for decision
tree learning from neural networks. For performance assessment, classification accuracy of
Single-test TREPAN, C4.5 and X-TREPAN were compared. In the scenarios discussed in the
paper, X-TREPAN model significantly outperformed the Single-test TREPAN and C4.5
algorithms in terms of classification accuracy as well as size, complexity and comprehensibility
of decision trees. To validate the results, we use classification accuracy not as the only measure
of performance, but also the kappa statistics. The kappa statistical values further validate the
conclusions that X-TREPAN is a better one in terms of decision tree induction.
REFERENCES
[1] A. B. Ticle, R. Andrews, M. Golea, J. Diederich The truth will come to light: Directions and
challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE
Trans. Neural Networks, vol. 9, no 6, pp. 1057-1068. 1998.
[2] G. Papantonopoulos, K. Takahashi, T. Bountis, B. G. Loos, Artificial Neural Networks for the
Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters. PLoS ONE 9(3): e89757.
(doi:10.1371/journal.pone.0089757), 2014.
[3] D. Puzenat, Behavior Analysis through Games Using Artificial Neural Networks, Third International
Conference on Advances in Computer-Human Interactions (IEEE), pp. 134–138, Saint Maarten,
France, 2010.
[4] M. Mano, G. Capi, N. Tanaka, S. Kawahara, An Artificial Neural Network Based Robot Controller
that Uses Rat’s Brain Signals, Robotics, 2,54-65, doi:10.3390/robotics2020054.
(www.mdpi.com/journal/robotics), 2013.
[5] E. V. Raghavendra, P. Vijayaditya, K. Prahallad, speech generation National Conference on
Communications (NCC), Hyderabad, India 2010. (doi: 10.1109/NCC.2010.5430190)
[6] A. Hossain, M. Rahman, U. K.Prodhan, F. Khan, Implementation Of Back-Propagation Neural
Network For Isolated Bangla Speech Recognition, International Journal of Information Sciences and
Techniques (IJIST) vol. 3, no. 4, 2013.
[7] S. Ayat, Z. A. Pour, Comparison between Artificial Neural Network Learning Algorithms for
Prediction of Student Average considering Effective Factors in Learning and Educational Progress,
Journal of mathematics and computer science 8, pp. 215 – 225, 2014.
[8] R. Andrews, J. Diedrich, A. Ticle, Survey and critique of techniques for extracting rules from trained
artificial neural networks, Knowledge-Based System, vol. 8, no 6, pp. 373-389. 1995.
[9] A. B. Ticke, R. Andrews, M. Golea, J. Diederich, The truth will come to light: Directions and
challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE
Trans. Neural Network, vol. 9 no. 6, pp. 1057 - 1068, 1998.
[10] A. Ticke, F. Maire, G. Bologna, R. Andrews, J. Diederich, Lessons from past current issues and
future research directions in extracting knowledge embedded in artificial neural networks in Hybrid
Neural Systems, New York: Springer-Verlag, 2000.
[11] H. Lohminger, Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999.
[12] C. Aldrich, Exploratory analysis of metallurgical process data with neural networks and related
methods, Elsevier 2002.
[13] StatSoft, Inc. (2004). Electronic Statistics Textbook. Tulsa, OK: StatSoft.
[14] M. M. Nelson, W.W. Illingworth, A Practical Guide to Nerual Nets. 4th ed. 1992.

[15] K. Plunkeet, J. L. Elman, (1997). Exercises in Rethinking Innateness: A handbook for connectionist
simulations, MIT Press, pp. 166.
[16] G.G. Towell, J. W. Shavli, Extracting Refined Rules from Knowledge-based Neural Networks,
Machine Learning, 13:71-101 1993.
[17] M.W Craven, J. W. Shavlik, Using sampling and queries to extract rules from trained neural
networks, Machine Learning. Proceedings of the Eleventh International Conference, Cohen W. W &
Hirsh H.(Eds), San Francisco, CA:Morgan Kaufmann 1995.
[18] L. Fu, Rule Learning by searching on adopted nets, In Proceedings of the 9th National Conference on
Artificial Intelligence, Anaheim, CA, pp.590-595, 1991.
[19] S. Thrums, Extracting rules from artificial neural networks with distributed representations, In
Advances in Neural Information Processing Systems 7, Cambridge, MA:Mit press, pp 505-512, 1995.
[20] M. W. Craven, Extracting Comprehensible models from trained Neural Network, PhD Thesis,
Computer Science Department , University of Wisconsin, Madison, WI, 1996.
[21] R. Andrews, J. Diederich, A. B. Tickle, A survey and critique of techniques for extracting rules from
trained neural networks. Knowledge Based Systems, 8(6), 373-389, 1995.
[22] D. Biggs, B. de Ville, E. Suen, A method of choosing multiway partitions for classification and
decision tree. Journal of Applied Statistics 18(1): 49-62, 1991.
[23] G. Liepins, R. Goeltz, R. Rush, Machine Learning techniques for natural resource data analysis . AI
Applications 4(3):9-18, 1990.
[24] M. W. Craven, J. W. Shavlik, Using sampling and queries to extract rules from trained neural
networks, Machine Learning. Proceedings of the Eleventh Inter-national Conference, Cohen W. W &
Hirsh H. (Eds), San Francisco, CA. 1995.
[25] M. W. Craven, J. W. Shavlik, Extracting tree-structured representations of trained networks. In
Advance in Neural Information Procession Systems, volume 8, pages 24-30, Denver, Com, MIT
Press. 1996.
[26] F. Chen, Learning accurate and understandable rules from SVM classifiers, M. Sc. Thesis, School of
computing science, Simon Fraser University. 2004.
[27] M.W. Craven, Extracting Comprehensible models from trained Neural Network, PhD Thesis,
Computer Science Department, University of Wisconsin, Madison, WI. 1996.
[28] B. Baesens, R. Setiono, C. Mues, J. Vanthienen, Using Neural Network Rule Extraction and
Decision Tables for Credit-Risk Evaluation management Science, vol. 49, no. 3, pp. 312, 2003.
[29] J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
[30] J. Ross Quinlan, ID3 Algorithm, Machine Learning, University of Sydney, book vol. 1, no.1, Syney,
Australia. 1975.
[31] E. Hunt, J. Marin, P. Stone, Experiments in Induction, New York, Academic Press, 1966.
[32] L. J. Breiman, R. Friedman, C. Olshen, Classification and regression trees, Wadsworth and Brooks,
Belmont, CA. 1984.
[33] L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and regression trees, Wadsworth and
Brooks, Belmont, CA. 1984.

X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible Decision Tree in Artificial Neural Network

Recommended

More Related Content

What's hot (18)

Viewers also liked (16)

Similar to X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible Decision Tree in Artificial Neural Network (20)

Recently uploaded (20)

X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible Decision Tree in Artificial Neural Network