Improving the performance of feature selection and data clustering with novel global search and elite-guided artificial bee colony algorithm

2019 ◽  
Vol 75 (8) ◽  
pp. 5189-5226 ◽  
Author(s):  
Zhenxin Du ◽  
Dezhi Han ◽  
Kuan-Ching Li
Keyword(s):  
Feature Selection ◽  
Data Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Global Search ◽  
Bee Colony

scholarly journals A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

2021 ◽  
Author(s):  
Jing Wang ◽  
Yuanzi Zhang ◽  
Minglin Hong ◽  
Haiyang He ◽  
Shiguo Huang
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Adaptive Method ◽  
High Dimensional ◽  
Feature Subset ◽  
Feature Selection Problem ◽  
Bee Colony ◽  
Dimensional Classification ◽  
Self Adaptive

Abstract Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

A new approach for data clustering using hybrid artificial bee colony algorithm

2012 ◽  
Vol 97 ◽  
pp. 241-250 ◽  
Author(s):  
Xiaohui Yan ◽  
Yunlong Zhu ◽  
Wenping Zou ◽  
Liang Wang
Keyword(s):  
Data Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
New Approach ◽  
Bee Colony

An Improved Method of Artificial Bee Colony Algorithm

2011 ◽  
Vol 101-102 ◽  
pp. 315-319 ◽  
Author(s):  
Xin Jie Wu ◽  
Duo Hao ◽  
Chao Xu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simulation Experiment ◽  
Optimization Problems ◽  
Local Extremum ◽  
Global Search ◽  
Improved Method ◽  
Object Function ◽  
Bee Colony ◽  
Better Than

The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.

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