Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University

1. Support Vector Machine Parameters
Tuning using Grey Wolf Optimization
Faculty f Computers and Information, Fayoum Universirt and SRGE member
Esraa M. El-hariri
http://www.egyptscience.net
Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of
Computers and Information, Cairo University

2. Overview
 Introduction
Problem Definition
Motivation
 Proposed Approach
 Results and Discussion
 Conclusion and Future Works
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3. Introduction
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4.  Support vector machines (SVMs) is one of the most
popular and widely used machine learning technique for
classification and regression.
 In many fields, SVMs has been successfully applied and
it proved that it is a powerful classification method and
find the best separation between classes.
Problem Definition
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5. Problem Definition
 However, in real practical applications, SVMs adoption
faces challenges. One of these challenges is the
selection of different SVMs parameters.
 Setting these parameters correctly helps at finding SVMs
models, which result in the best classification accuracy.
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6. Problem Definition
 There are two types of parameters (SVMs penalty
constant C parameter and the parameters in kernel
function such as width parameter of RBF kernel
function), and the values of these parameters affect the
performance of SVMs.
 So, parameters tuning is very important for any
classification problem.
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7. Motivation
 The aim of this research is:
 To present a hybrid model that employs grey wolf optimizer
(GWO) along with support vector machines (SVMs)
classification algorithm to improve the classification
accuracy via selecting the optimal settings of SVMs
parameters.
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8. Proposed
Approach
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9. SRGE workshop in Cairo University Conference Hall (7-November-
General PSO
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10. 10
Solution structure
Proposed
Approach

11. Results
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12. Used Dataset
 The proposed approach is tested on a real practical
application, which is classifying different ripeness stages of
bell pepper. Color and texture information are very helpful in
ripeness prediction and freshness examination of fruits.
 For many crops such as tomato and bell pepper, one of the
most significant criteria related to fruit identification and fruit
quality is surface color. Also, it is a good indicator for
ripeness.
 The used dataset consists of Color features for five ripeness
stages of Bell Pepper.SRGE workshop in Cairo University Conference Hall (7-November-
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13. Experimental Results
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14. Conclusion and Future Works
 Experimental results indicated that the proposed GWO-SVMs
approach outperformed the typical SVMs classification
algorithm with classification accuracies of 92% for RBF and
linear kernel functions.
 Accuracy achieved by SVMs MLP kernel function is
increased by ≈ 20.57%.
 For linear kernel function, accuracy is increased by only
1.14%. Also, for RBF kernel function, accuracy is increased
by ≈ 6.86%.
 Finally, for polynomial kernel function, accuracy is increasedSRGE workshop in Cairo University Conference Hall (7-November-
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15. Conclusion and Future Works
 For future research, variety of challenges and research
directions could be considered. Some general research
directions are to consider applying the approach proposed in
this article to other machine learning techniques, which
contain parameters to be optimized.
 Another open problem is to tackle the second problem, which
faces SVMs or any classification system; namely feature
selection, using PSO. Moreover, a hybrid approach for
optimizing SVMs parameters and select best features subset
is planned to be developed.
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16. Thanks and Acknowledgement
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