New Feature Selection Model Based Ensemble Rule Classifiers Method for Dataset Classification Full Text

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.8, No.2, March 2017
DOI : 10.5121/ijaia.2017.8204 37
NEW FEATURE SELECTION MODEL-BASED
ENSEMBLE RULE CLASSIFIERS METHOD FOR
DATASET CLASSIFICATION
Mohammad Aizat bin Basir1
and Faudziah binti Ahmad 2
1
Universiti Malaysia Terengganu (UMT)Terengganu, Malaysia
2
Universiti Utara Malaysia(UUM) Kedah, Malaysia
ABSTRACT
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
KEYWORDS
Feature Selection, Attribute, Ensemble, and Classification.
1. INTRODUCTION
Real world dataset usually consist a large number of attributes. It is very common some of those
input attributes could be irrelevant and consequently give an impact to the design of a
classification model. In situations where a rule has too many conditions, it becomes less
interpretable. Based on this understanding, it becomes important to reduce the dimensionality
(number of input attributes in the rule) of the rules in the rule set. In practical situations, it is
recommended to remove the irrelevant and redundant dimensions for less processing time and
labor cost. The amount of data is directly correlated with the number of samples collected and
the number of attributes. A dataset with a large number of attributes is known as a dataset with
high dimensionality [1]. The high dimensionality of datasets leads to the phenomenon known as
the curse of dimensionality where computation time is an exponential function of the number of
the dimensions. It is often the case that the model contains redundant rules and/or variables.
When faced with difficulties resulting from the high dimension of a space, the ideal approach is to
decrease this dimension, without losing the relevant information in the data. If there are a large
number of rules and/or attributes in each rule, it becomes more and more vague for the user to
understand and difficult to exercise and utilize. Rule redundancy and/or attribute complexity
could overcome by reducing the number of attributes in a dataset and removing irrelevant or less
significant roles. This can reduce the computation time, and storage space. Models with simpler
and small number of rules are often easier to interpret.

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The main drawback of rule/attributes complexity reduction is the possibility of information loss.
It is important to point out that two critical aspects of the attribute reduction problem are the
degree of attribute optimality (in terms of subset size and corresponding dependency degree) and
time required to achieve this attribute optimality. For example, existing methods such as Quick
Reduct and Entropy-Based Reduction (EBR) methods find reduced in less time, but could not
guarantee a minimal subset [1] –[3] whereas other hybrid methods which combine rough sets and
swarm algorithm such as GenRSAR, AntRSAR, PSO-RSAR and BeeRSAR methods improve the
performance but consume more time [1], [2].
In feature selection, also known as variable selection, attribute selection or variable subset
selection is the process of selecting a subset of relevant features (attributes) for use in model
construction. It is the process of choosing a subset of original features so that the feature space is
optimally reduced to evaluation criterion. Feature selection can reduce both the data and the
computational complexity. The raw data collected is usually large, so it is important to select a
subset of data by creating feature vectors. Feature subset selection is the process of identifying
and removing much of the redundant and irrelevant information possible.
However, the use of a subset of a feature set may disregard important information contained in
other subsets. Consequently, classification performance is reduced. Therefore, this paper aims to
find the optimal set of attributes and improve the classification accuracy by adopting the
ensemble classifier method. Firstly, an optimal set of attribute subsets are extracted by applying
various search method and a reduction algorithm to the original dataset. Then an optimal set of
attributes further classified by adopting a classification ensemble approach. In the experiment, 6
various datasets were used. The experiment results showed that the performance of the ensemble
classifier was improved the classification accuracy of the dataset. This paper is organized as
follows: in Section II, related works are discussed. The proposed methodology is presented in
Section III. In Section IV, the results and discussion are given. Finally, the conclusions presented
in Section V.
2. RELATED WORKS
There many research in feature selection methods for constructing an ensemble of classifiers. The
ensemble feature selection method is where a set of the classifiers, each of which solve the same
original task, are joined in order to obtain a better combination global classifier, with more
accurate and reliable estimates or decisions than can be obtained from using a single classifier.
The aim of designing and using the ensemble method is to achieve a more accurate classification
by combining many weak learners.
Previous studies show that methods like bagging improve generalization by decreasing variance.
In contrast, methods similar to boosting achieve this by decreasing the bias [4]. [5] demonstrated
a technique for building ensembles from simple Bayes classifiers in random feature subsets.
[6] explored tree based ensembles for feature selection. It uses the approximately optimal feature
selection method and classifiers constructed with all variables from the TIED dataset.
[7] presented the genetic ensemble feature selection strategy, which uses a genetic search for an
ensemble feature selection method. It starts with creating an initial population of classifiers where
each classifier is generated by randomly selecting a different subset of features. The final
ensemble is composed of the most fitted classifiers.
[8] suggested a nested ensemble technique for real time arrhythmia classification. A classifier
model was built for each 33 training sets with enhanced majority voting technique. The nested
ensembles can relieve the problem of the unlikelihood of a classifier being generated when

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learning the classifier by an old dataset and limited input features. One of the reasons that make
the ensemble method popular is that ensemble methods tend to solve dataset problems.
3. METHODOLOGY
Figure 1. Methodology
The methodology is shown in Fig. 1. It consists of five (5) steps: (1) data collection; (2) data pre-
processing; (3) dimensionality reduction; (4) classify an optimal set of attributes by using the
ensemble rule classifier method; (5) Result-improved classification accuracy: ensemble rule
classifier methods have been compared with datasets that do not use the ensemble rule classifier
method. The output of phase 1 (step 1 – 3) is the optimal set of attributes. For phase 2 (step 4 – 5),
the output is the improved classification accuracy by adopting an ensemble rule classifier method
for the classification task. The details of the steps involved are described below:-
Step 1 (Data Collection): Six (6) different datasets were selected from UCI Machine Learning
Repository. Arrhythmia dataset is one of the dataset selected due to its many features that make it
challenging to explore [9]. Other five (5) datasets also were taken from different domain in order
to confirm the suitability of the ensemble classifiers.
Step 2 (Data Pre-processing): Dataset that has missing values has been pre-processed in order to
make sure that the dataset is ready to be experimented. All datasets were discretized since it has
numeric data but needs to use classifier that handles only nominal values.
Step 3 (Dimensionality Reduction): 8 search methods and 10 reduction algorithms have been used
in order to get the optimal set of attributes. The output of this step is the model consist an optimal
set of attributes.

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Step 4 (Classify optimal set of attributes by using the ensemble rule classifier method): In this
step, the optimal sets of attributes obtained from previous step were classified by adopting the
ensemble classifier method.
Step 5 (Model with good accuracy): In this step, the performance (% classification accuracy) of
the dataset that used ensemble rule classifier methods has been compared with datasets that do not
use the ensemble rule classifier method. The output of this step is the improved classification
accuracy with optimal number of attributes.
Standard six datasets namely Arrhythmia, Bio-degradation, Ionosphere, Ozone, Robot Navigation
and Spam-base from the UCI [10] were used in the experiments. These datasets include discrete
and continuous attributes and represent various fields of data. The reason for choosing this dataset
is to confirm the ensemble classifier is suited to all fields of data. The information on the datasets
is shown in Table I.
Table 1. Dataset Characteristics.
All six (6) datasets were tested using 8 search methods and 10 reduction algorithms.
4. RESULTS AND DISCUSSION
The outputs for phase 1 and phase 2 are presented in this section. The performance results are
presented in the percentage of classification accuracy with the optimal set of attributes.
4.1. PHASE 1 (STEP 1 – 3)
Table 2. List of an optimal set of attributes selected.
Table 2 shows the results of an optimal set of attributes selected by using various search method
and reduction algorithm. In phase one (1), eight (8) search methods, namely Best First Search,
Genetic Search, Exhaustive Search, Greedy Stepwise Search, Linear Forward Selection Search,
Scatter Search, Subset Size Forward Selection Search and Ranker Search were applied. In
addition, ten (10) reduction algorithms that are CfsSubsetEval, ClassifierSubsetEval,
ConsistencySubsetEval, FilteredSubsetEval, ChisquaredAttributeEval, FilteredAttributeEval,
GainRatioAttributeEval, InfoGainAttributeEval, PrincipalComponent and WrapperSubsetEval
were adopted. It can be seen that Arrhythmia and Ozone dataset produced a massive attribute
reduction, which is more than 90% reduction. Best first search (BSF) was used with
WrapperSubsetEval for Arrhythmia dataset since BFS is a robust searching [11] and better for
dataset studied [12]. The rest of the dataset achieved more than 60% attribute reduction. Wrapper

41
method (WrapperSubsetEval) performed better for 4 out of 6 datasets selected with combination
of various search method. These experiments confirmed that significance attribute reduction can
be accomplished by combining the right search method and reduction algorithm.
4.2. PHASE 2 (STEP 4 – 5)
In phase 2, each selected set of attributes for the six (6) various dataset namely Arrhythmia, Bio-
degradation, Ionosphere, Ozone, Robot Navigation and Spam-base were classified using
ensemble rule classifier methods of boosting, bagging and voting. In this phase, rule classifiers
like Repeated Incremental Pruning to Produce Error Reduction (RIPPER), PART, Prism, Nearest
Neighbor With Generalization (NNge) and OneR were evaluated with ensemble method. 70% of
the dataset being used as training and the remaining 30% was used for testing data. The results
are shown in Table 3 through Table 6.
Table 3. Classification Result of using RIPPER and RIPPER with Ensemble Rule Classifier Method.
Table III shows the classification result of using RIPPER and RIPPER with the ensemble method.
RIPPER [13] with boosting and bagging method improves the classification accuracy of 4
datasets namely Bio-degradation, Ionosphere, Robot Navigation and Spam-base. These results are
in line with the strength of the RIPPER that it tries to increase the accuracy of rules by replacing
or revising individual rules [14]. It uses a reduced error pruning, which isolates some training data
in order to decide when to stop adding more conditions to a rule. It also used a heuristic based on
the minimum description length principle as stopping criterion.
Table 4. Classification Result of using PART and PART with Ensemble Rule Classifier Method
Table 4 shows the classification result of using PART and PART with ensemble method. PART
rule classifier with bagging method increased the classification accuracy of all the datasets. The
PART algorithm [15] is a simple algorithm that does not perform global optimization to produce
accurate rules. It adopts the separate-and-conquer strategy by building a rule, removes the
instances; it covers, and continues creating rules recursively for the remaining instances until
there are no more instances left. In addition, many studies have shown that aggregating the
prediction of multiple classifiers can improve the performance achieved by a single classifier
[16]. In this case, Bagging is known as a “bootstrap” ensemble method that creates individuals for
its ensemble by training each classifier on a random redistribution of the training set. In contrast,

42
Boosting method with PART rule classifier performed better accuracy for Robot Navigation
dataset with more than 3% accuracy. In this case, these results are consistent with data obtained in
[17] which proved that PART algorithm is the effective algorithm to be used for classification
rule hiding.
Table 5. Classification Result of using PRISM and PRISM with Ensemble Rule Classifier Method
Table 5 shows the Classification result of using Prism and Prism with the ensemble rule classifier
method. Prism is an algorithm used different strategy to induce rules which are modules that can
avoid many of the problems associated with decision trees [18]. Prism rule classifier with bagging
method performed well to enhance all the dataset. In addition, boosting method with Prism
produced better accuracy result for Ionosphere and Spam-base Dataset.
Table 6. Classification Result of using OneR and OneR with Ensemble Rule Classifier Method
Table 6 shows the classification result of using OneR and OneR with the ensemble rule classifier
method. Boosting Method with OneR rule classifier performed a lot better accuracy for Bio-
degradation, Ionosphere, Robot Navigation and Spam-base dataset. Huge accuracy improvement
using OneR rule classifier with Boosting method for Spam-base dataset which is more than 10%
accuracy increased. In this case, OneR demonstrated the efficacy as an attribute subset selection
algorithm in similar cases in [20].
In summary, results have shown significant improvement in term of classification accuracy when
using the ensemble rule classifier method.
5. CONCLUSIONS
In this paper, eight (8) search methods with ten (10) reduction algorithms were tested with 6
datasets. Experimental results benchmark dataset demonstrates that the ensemble method, namely
bagging and boosting with rule classifiers which are (RIPPER), PART, Prism, (NNge) and OneR
significantly perform better than other approaches of not using the ensemble method. Beside
these, it is found that right combination between search methods and reduction algorithms shown
good performance feature selection model on extracting an optimal number of attributes. For
future research, methods of finding the suitable match between search method, reduction
algorithm and ensemble classifiers can be developed to get a better view of the datasets.

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ACKNOWLEDGEMENTS
The authors wish to thank Universiti Malaysia Terengganu (UMT), Universiti Utara Malaysia
(UUM) and Kementerian Pendidikan Malaysia (KPM). This work was supported by UMT, UUM
and KPM, Malaysia.
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AUTHORS
Mohammad Aizat Bin Basir is currently a lecturer in Universiti Malaysia Terengganu (UMT), Malaysia.
Faudziah Binti Ahmad is currently Assoc. Prof. in computer science in Universiti Utara Malaysia (UUM),
Malaysia.

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