Support vector machine parameters tuning using grey wolf optimization

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

Overview

Introduction Problem Definition Motivation

Proposed ApproachResults and DiscussionConclusion and Future Works

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SRGE workshop in Cairo University Conference Hall (7-November-2015)

Introduction

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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 4


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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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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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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Proposed Approach

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General PSO9

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Solution structure

Proposed Approach

Results

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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-2015)

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Experimental Results

SRGE workshop in Cairo University Conference Hall (19-September-2015)

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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 increased by ≈ 12%.


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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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Thanks and Acknowledgement16


Engineering

Support vector machine parameters tuning using grey wolf optimization