Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Gürcan Yavuz,Doğan Aydin

doi:10.1155/2016/9569161

Gürcan Yavuz, Doğan Aydin

Open Access

https://doi.org/10.1155/2016/9569161

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Abstract

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.

Highlights

Many data mining and machine learning applications suffer from the curse of dimensionality in which a dataset usually involves a large number of features, often including relevant and irrelevant features [1]
The six Artificial Bee Colony (ABC) algorithms are selected for performance comparison, namely, original ABC (OABC), modified ABC (MABC), enhanced ABC (EABC), Gbest guided ABC (GABC), chaotic ABC (CABC), and GbestDist guided ABC (GDABC)
We introduced angle modulation technique for feature subset selection

Summary

Introduction

Many data mining and machine learning applications suffer from the curse of dimensionality in which a dataset usually involves a large number of features, often including relevant and irrelevant features [1]. The paper uses only original ABC and does not give any information about the generation of bit vector used in feature selection Another binary ABC algorithm for feature selection is proposed in [5] but the search equation of the binary ABC algorithm is based on modifying candidate solution without interacting with the other solutions. The algorithm turns to a randomized algorithm which randomly generates solutions without interaction in population In these approaches, candidate solutions are presented with a bit vector of size n. ABC algorithms employ angle modulation based bit vector generation for feature selection for the first time. In angle modulation based approach, an ABC algorithm, called Angle Modulated Artificial Bee Colony (AMABC) algorithm, selects candidate feature sets with a bit vector obtained by a bit string generator employing a trigonometric function.

Artificial Bee Colony Algorithm

Angle Modulated Artificial Bee Colony Algorithms

Experimental Results

Conclusions