IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Jiahui Li,Geng Sun,Wei Zhang,Hui Kang,Bai Ji,Tie Feng,Wenqi Li

doi:10.1109/access.2020.3001204

Abstract

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

Highlights

The availability of large scale datasets has boosted the applications of machine learning in many fields, such as active matter [1], molecular and materials science [2], and biomedical [3]
3) Experiments based on the UC Irvine Machine Learning Repository are conducted to evaluate the performance of the proposed improved binary dragonfly algorithm (IBDA) for feature selection, and the results are compared to some other algorithms
IBDA introduces the EPD_LRS mechanism, AC operator and v-shape binary scheme to improve the performance of conventional dragonfly algorithm (DA) and make it suitable for the formulated feature selection problem

Summary

Introduction

The availability of large scale datasets has boosted the applications of machine learning in many fields, such as active matter [1], molecular and materials science [2], and biomedical [3]. With the increasing of complexities of the machine learning models, more and more datasets with high-dimensional feature spaces are generated. Is to select the informative subset from a high-dimensional feature space [5], such that the number of features can be reduced and the worthless features can be deleted, thereby saving the computing resources and increasing the classification accuracy for machine learning. Feature selection methods are mainly divided into two categories that are filter and wrapper approaches. The filter-based method assigns a relevance score to each feature by using a statistical measure. For the wrapper-based approach, it utilizes a classifier to guide the feature selection results and the accuracies of this method are usually better than the filter-based method. The wrapper-based approach is an efficient method for feature

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Conclusion