Hybrid Information Gain Method and Bagging in Data Classification

using Support Vector Machine

Immanuel H. G. Manurung, Tulus and Poltak Sihombing

Departement of Computer Science and Information Technology, University of Sumatera Utara,

Jl. Dr. T. Mansur No.9, Medan, Indonesia

Keywords: Selection Attributes, SVM, Bagging, IG, Fold Cross-Validation

Abstract: The selection process is very influential attributes of dataset in SVM algorithm which tends to produce good

accuracy on the results of the classification (classifier) SVM is not optimal. To reduce the effect of selection

attributes on SVM classifiers, it is necessary to apply a combination of methods of feature selection algorithms

that are Bootstrapping Aggregation (Bagging) and methods of Information Gain (IG). The application of the

algorithm Bagging the feature selection is done to give weight to each feature are recommended, so that the

found feature is a strong classifier, whereas IG focuses on identifying attributes and evaluate the impact of a

beneficial features based on ranking the features that can be recommended to the classifier SVM in the process

classification. Experiments implementation of Information Gain feature selection techniques that use

attributes with election threshold level. The results showed that, the performance accuracy of SVM classifiers

in dataset by combining IG before bagging process, by setting the value thresold >= 0.02 and a 10-fold cross-

validation, show that with the implementation of information gain feature selection techniques can improve

the performance of machine learning classification algorithm.

1 INTRODUCTION

1.1 Background

Data mining technology is one tool for data mining in

large data bases and the specification level of

complexity that has been widely used in many

application domains such as banking, fraud detection

and telecommunications fields .Some research has

also been done using data mining techniques to gather

information from a database, such as to analyze the

performance (performance) of students in the

learning process (Kalles & Pierrakeas, 2006) as well

as to help teachers to manage classes diampunya

(Agathe & Kalina, 2005) and it is possible to analyze

and evaluate the academic data for know the quality

of higher education (AlRadaideh et al, 2006).

Selection of the right features and the classifier is

essential in improving the accuracy and computing in

the classification. Feature selection technique is done

to reduce the irrelevant features and reduce the

dimensions of the features in the data. A feature

selection techniques to reduce the dimension

attributes. The dimensional reduction is done to get

the attributes that are relevant and not excessive so as

to speed up the classification process and can improve

the accuracy of classification algorithms. (Arifin,

2015). Feature selection method used in this research

is the Information gain. The method will perform

computing process to obtain the attributes that most

influence on the dataset. Dinakaran et al implement

feature selection techniques Information Gain

method of ranking the best features and classification

Decision Tree algorithm J48 (Dinakaran et al, 2013).

Ramaswami and Bhaskaran conduct a comparative

study of five feature selection techniques and apply

four classification algorithms in data mining

education. The experimental results indicate that

Information Gain feature selection techniques

showed the best results (Ramaswami & Bhaskaran,

2009).

In this study, used Information Gain for feature

selection and bagging to improve the accuracy of

classification in machine learning. "Bagging is a

method that can improve the results of the

classification of machine learning algorithms

(Breimann, 1994)". "This method formulated by Leo

Breiman and the name is inferred from the phrase"

Bootstrap aggregating "(Breimann, 1994)". Bagging

194

Manurung, I., Tulus, . and Sihombing, P.

Hybrid Information Gain Method and Bagging in Data Classiﬁcation using Support Vector Machine.

DOI: 10.5220/0010040401940201

In Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology (ICEST 2018), pages 194-201

ISBN: 978-989-758-496-1

is a method based on the ensemble method, several

studies applying meta-algorithm, these include the

research for planning and environmental changes

(Kang et al, 2016), research for the development of

systems to track visual (Chang and Hsu, 2015),

enhancing the performance related to research in the

field of pharmacy (Galvan, 2015), improved

performance in the field of voice processing (Chen,

2015),

Therefore, in this study the proposed Information

Gain for feature selection algorithms and meta

bagging to improve the classification performance of

SVM method. Two stages in building a hybrid

scheme, the first step is the selection of the features

considered relevant for classification and there

construct the second phase of the hybrid intelligent

model scheme. Further features that are considered

relevant will be used as input in the classification with

SVM.

1.2 Problem Formulation

Issues to be addressed in this study is how much the

value of accuracy obtained from the application of

Information Gain for feature selection and bagging to

improve the accuracy of classification in machine

learning using Support Vector Machine (SVM) in the

form of hybrid models.

1.3 Limitations

Boundary problem discussed in this research is to

implement a meta model of machine learning bagging

on classification Support Vector Machine (SVM)

with Information Gain feature selection.

1.4 Research Objectives

The purpose of this study is to model meta bagging

on classification machine learning Support Vector

Machine (SVM), to improve the accuracy of each

algorithm values and compare them.

1.5 Benefits Research

The benefits of this research are:

1. Can be used as reference material to expand

horizons in research specifically in the

classification of machine learning.

2. Can be used to provide the best information in the

selection of attributes to get the attributes that

most influence on the dataset.

2 LITERATURE

2.1 Data Mining

Data mining is a concept used to find hidden

knowledge in the database. Data mining is a semi-

automatic process that uses statistical techniques,

mathematics, artificial intelligence, and machine

learning to extract and identify potential knowledge

and useful information that is stored in large

databases. Data mining is part of the process of KDD

(Knowledge Discovery in Databases), which consists

of several stages such as the selection of data, pre-

processing, transformation, data mining, and

evaluating results. In terms of other data mining is

also

defined as the process to obtain useful

information from large data base warehouse.

Data mining is a series of processes for adding

additional value of a set of data in the form of

knowledge that had been unknown to them manually.

Said mining means that efforts to get a bit of valuables

from a large number of basic material. Data mining is

the process of finding patterns and relationships

hidden in large amounts of data for the purpose of

classification, estimation, prediction, association rule,

clustering, description and visualization. In a simple

data mining can be regarded as a process of filtering

or "mining" knowledge from large amounts of data.

The process and data filtering technique determines

the quality of knowledge and information that will be

obtained. Another term for data mining is Knowledge

Discovery in Databases (KDD).

Data cleaning

Cleaning of the data was performed to remove noise

and inconsistent data

Data integration.

Data integration process undertaken to

menggabungkandata from various sources.

Data Selection.

Selection data is done to retrieve the relevant data,

which will be used for process analysis in data

mining.

Data transformation.

This process is done to transform the data into the

proper form for in-mine.

Data Mining.

Data mining is the process for applying a

metodeuntuk extract patterns in the data.

Evaluation pattern.

Evaluation of the pattern is needed to identify some

interesting patterns representing knowledge.

Presentation of knowledge.

Represents the knowledge that had been dug up to the

user to visualize such knowledge.

Hybrid Information Gain Method and Bagging in Data Classiﬁcation using Support Vector Machine

195

2.2 Support Vector Machine

Support Vector Machine(SVM) was developed by

Boser, Guyon, Vapnik, and was first presented at the

1992 Annual Workshop on Computational Learning

Theory. The basic concept of Support Vector

Machine (SVM) is actually a harmonious

combination of theories of computing has been

around for decades before, such as margin hyperplane

(Duda & Hart 1973, Cover 1965, Vapnik 1964, and

others), the kernel was introduced by Aronszajn 1950,

and likewise with the concepts supporting the other.

However, until 1992, there has never been an attempt

assembling these components.

Concept of Support Vector Machine (SVM) can

be explained simply as an attempt to find the best

hyperplane which serves as a separator of two classes

in the input space. pattern that is a member of two

classes: +1 and -1 and share alternative dividing lines

(boundaries discrimination). Margin is the distance

between the hyperplane to the nearest pattern of each

kelas.Pattern closest is called a support vector.

Attempts to locate this hyperplane is the core of the

learning process on SVM (Christianini, 2000).

Initialization of training data is used, given the

label in the form of





, with



Rxy  ,1,1

using formulations C-SVM.

For the linear case, the algorithm is shown to find the

best hyperplane to separate the data by minimizing

the following function:









Cww



(2.1)

for



iii

bxwy



 1

and

0



, Where C>

0 is a trade off of the constraints. Kernel function K

data point (x, z): Rn x Rn R is the result of inner

product of

 





, Data mapping function





and





mentioned very difficult to find

values of high yield dimensional inner product value

is equal to K (x, z). Because of these difficulties, the

system of direct control of the kernel function is used.

Then to get the optimal hyperplane equation, can be

used the following equation:

 







iji

jii

xxKyyW



(2.2)

for :





and









(2.3)

the function of the decision is



xfsign

, Where :







bxxKyxf

iii





1



(2.4)

Note that Equation 3 only require access to the

kernel function alone, without having to perform

mapping data by function





, M is the number of

support vectors. So this allows one to solve

formulations in high-dimensional feature space with

a highly efficient, step is called the kernel trick. For

linear kernel, we can use the kernel functions





zxzxK





And function hyperplane





bxwxf







, Where the vector w can be

calculated by the formula





iii

xyw



and







 xwxwb ..

, If in case of non-linear

vector w can be calculated by the formula









iii

xyw



and the constant b by the

formula

 







 xwxwb



Additive selected specifically for the kernel to use the

formula









zxKzxK

),(,

and



can

be written as





bxwxf 







2.3 Meta-algorithm

Meta-algorithm is an algorithm that uses another

algorithm as a representative, and also an algorithm

that has a sub-algorithms as variables and parameters

can be changed. Examples include meta-algorithm is

boosting, simulated annealing, bootstrap aggregating,

AdaBoost, and random-restart hill climbing.

2.3.1 Algorithm boosting

Boosting is an meta-algorithm in machine learning

to perform supervised learning. Boosting theory

introduced by questions Kearns (1988): Can a set of

weak learner creates a strong unity learner? Weak

learner is a classifier that has little correlation with

actual classification, while strong learner is a

classifier which has a strong correlation with the

actual classification.

Most of boosting algorithms follow a plan.

Generally boosting occurs in iterations, incrementally

adding weak learner into a strong learner. At each

iteration, the weak learner to learn from a workout

data. Then, the weak learner was added to the strong

learner. After a weak learner is added, the data is then

converted their respective weights. The data were

misclassified will experience weight gain, and the

data were classified correctly will experience a

ICEST 2018 - 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and

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196

reduction in weight. Therefore, the weak learner on

the next iteration will be more focused on data

misclassified by the weak learner that before.

Of the many variations of boosting algorithm,

AdaBoost is the most famous in the history of its

development, is the first algorithm that can adapt to

the weak learner. However, there are some examples

of other boosting algorithms such as LPBoost,

TotalBoost, BrownBoost, MadaBoost, LogitBoost,

and others. Most boosting algorithm can be

incorporated into the framework AnyBoost.

2.3.2 Metode Bagging (Bootstrap

Aggregating)

This combined method is a method models each of

which is a combination of a lineup that includes a

number of k-models of the M

, M

..., M

, With the

aim to create an increase in the combined models, M

*. In general there are two methods of combination

of models, namely Bagging and Boosting. Both can

be used in the process of classification and

prediction (Han and Kamber, 2006). Illustrations

combined methods can be seen in Figure 2.1.

Figure 2.1: Illustration Model Combination Methods

Pada Figure 2, there are descriptions of the work

steps of the method combination of models, where in

the method aims to improve the accuracy of the model

consisting of Bagging and Boosting method. This

method produces a classification or prediction

models, M

, M

..., M

, Where voting strategy

against emerging choice is a combination of strategies

used to combine the predictions for the object that has

been given the unknown category.

Bagging method is the theory proposed by

Breiman (1996), which is based on the concept of a

bootstrap theory and aggregating that combine both

benefits into a single theory. Bootstrap is applied

based on the theory of random sampling with

replacement (Tibshirani and Efron, 1993). In effect,

the model in engineering classification (classifier)

that has been formed is likely to have a better

performance. Understanding of the process of

aggregating is incorporating some of the classifier.

Sometimes combined classifier gives better

results compared to the classifier only, due to the

incorporation of both the benefits of some of the

classifier at the end Bagging penyelesaiannya.Oleh

therefore helpful to help build or establish a better

classifier on the training data samples. Here are the

stages of implementation Bagging technique:

1. Perform as many Xb bootstrap replication of a

number n m training data. Repeat these steps for

1,2, ..., B. Where m is the number of data that is

taken from the training data, n is the sample size

of the training data and B is the number of

bootstrap replication is done.

2. By using a simple majority vote, been labeled the

most widely emerged from the assessment results

as a rule for making a final decision.

2.4 Hybrid Algorithm

Hybrid or Hybrid Algorithm algorithm is an

algorithm that combines two or more methods into

one or several models combined to produce items

according to the user's wishes (Burke, R., 2007).

Broadly speaking, this algorithm has two stages. The

first stage is the algorithm generate node ordering and

second stages of the algorithm constructs a structure.

3 RESEARCH METHODOLOGY

All data is primary data collected from UCI Machine

Learning Repository, Research steps are generally

depicted in the flow diagram Figure 3.1

Figure 3.1 Block diagram of the stages of research

Hybrid Information Gain Method and Bagging in Data Classiﬁcation using Support Vector Machine

197

There are several stages in this study, the dataset

collection, pre-processing, a single model, and hybrid

models. After data collection, pre-processing is done

data by eliminating the data contained missing value.

In the single phase model of the result data

preprocessing The classified using SVM. The final

stage is to establish a scheme hybrid intelligent model

to produce some hybrid combinations. At the stage of

the model development hybrid, there is a feature

selection to select features considered relevant as an

input for classification.

3.1 Variable Data and Research

This study will use data on Diabetes Diagnosis of UCI

Machine Learning Repository. Overall, the data

Diabetes Diagnosis contains 9 attributes described in

the following table.

Table 3.1.

No. Attribute

nancies

2. PG Concentration

3. diastolic BP

4. Tri Fold Thic

5. serum Ins

6. BMI

7. DP Function

9 A

10. diagnosis

4 RESULTS AND DISCUSSION

In this section explored further on the implementation

of the proposed system architecture. In testing, the

evaluation techniques used cross validation models to

observe and analyze the performance measurement

results. Tests were conducted at four methods of

classification. In each method to do a comparison

between the performance of the classification by

application of meta-adaptive boosting algorithm and

without the application of this meta-algorithm. To

measure the performance of this classifier is used

seven sizes, namely accuracy, Kappa, MAE,

Precision, Recall, F-Measure, and the ROC.

4.1 Read Data Training

Training data used contains as many as 768 data line

containing the seven attributes that have been

described in the early chapters. To specify the data

that will be analyzed then the first step is to read

training data.The following are some of the training

data will be used.

4.2 Testing Single Model SVM Method

From a probability value above 768 will be tested as

much data as the data and completed using weka tools

so that the resulting classification result as in Figure

4.2.



Figure 4.4: Testing Results with SVM method

a. Accuracy obtained is 77.3438% of total

Correctly classified instances as many as 594.

b. Number of instances was incorrectly classified

as much as 174 or 22.6563%.

c. The results of the root of the mean squared

error is 0.476.

4.3 Testing Results with Information

Gain

From the calculation of the value of the determined

value overlapping provisions of the discriminant

(discriminant ability). Average - average value of the

provisions of the discriminant (discriminant ability)

of each feature on each - each class is divided by the

number of changes made reference in calculating the

suspension of each feature. Where the features with

the smallest score is the highest rating in ranking and

can be recommended in the classification process.

The following ranking of feature selection using the

IG shown in Table 4.2.

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Table 4.2: Rating Each Attribute

Ranked Attribute Score

1 PG Concentration 0.1901

2 BMI 0.0749

3 Age 0.0725

4 serum Ins 0.0595

5 Tri Fold Thic

 0.0443

nancies 0.0392

7 DP Function 0.0208

8 diastolic BP 0014

In the third experiment, started by applying feature

selection using the IG. The results of ranking the

features of the IG, the percentage of moderately by

70% the results of the second experiment had the

highest accuracy value will be recommended to the

bagging algorithm. From the selection by bagging

features that the Hx value equal to +1 is decent

features recommended and the value is equal to -1 Hx

is a feature that is not recommended in classification.

Here are the results of measurement with IG before

bagging shown in Table 4.3.

Figure 4.5: Selection Attributes with the Information Gain

Information gain feature selection techniques applied

by using perangkingan attributes by using the selected

threshold level features by using threshold >= 0,02.

4.4 Testing with Bagging - SVM

In the fourth experiment begins by applying feature

selection using bagging to perform weighting on

seven features recommended so that a strong

classifier and further evaluation of the impact of a

beneficial features based on ranking features using

IG. The measurement results of ranking the fourth

feature selection in the experiment are shown in Table

4.5.

Figure 4.6: Testing Results with bagging-SVM models

In the figure indicates that the value of the highest

accuracy by applying feature selection IG after

bagging in a 10-fold cross-validation is equal to

82.14%. From the results of measurements performed

some experiments can be concluded that the increase

in performance accuracy, precision and sensitivity

(recall) SVM models, are affected by the application

of feature selection in the SVM classifier and the

determination of the number-fold cross validation.

That is, if the value-fold cross validation the greater

the value of accuracy, precision and sensitivity

(recall) tends to increase and will get better reliability

SVM models.

4.5 Results Discussion

In the experimental study, there are two models:

Single classification results using SVM for hybrid

models while there are 2 classification accuracy

results, respectively for hybrid models with bagging

classifier. For hybrid models bagging scheme can

improve accuracy. Accuracy using only one classifier

was 77.34% and increased accuracy using hybrid

scheme with bagging-SVM amounted to 81.14%.

Because the hybrid scheme has fewer features in

addition to mengkatkan accuracy, computation

process will be faster. This shows that by using more

features does not improve accuracy, but by choosing

the features that are considered relevant so as to

reduce the size of features with the right method and

the right classifier will improve accuracy and speed

up the computation.

Hybrid Information Gain Method and Bagging in Data Classiﬁcation using Support Vector Machine

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5 CONCLUSION

5.1 Conclusion

Based on the results of experimental studies hybrid

model with bagging method for classifying a number

of conclusions as follows:

1. Bagging highly adaptive method with the

features, because each iteration in bagging an

election classifier that has the smallest error.

bagging choose the best feature in every

iteration. So with many or few features that are

used or with any data, bagging would classify

properly.

2. The research concluded that the hybrid scheme

with classifier bagging on classification has

been proven to improve accuracy and speed up

the computation. By using a single classifier

accuracy of 77.34% increased by 81.14% using

a hybrid scheme.

5.2 Suggestions

For further research, bagging metaalgoritme will be

developed that are not too sensitive to outliers (data

outliers), so that optimal performance of

metaalgoritme over again.

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