Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

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.

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

10.21203/rs.3.rs-1123323/v1 ◽

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.

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A Centre of Gravity-Based Preprocessing Approach for Feature Selection Using Artificial Bee Colony Algorithm on High-Dimensional Datasets

Lecture Notes in Electrical Engineering - Advances in Communication Systems and Networks ◽

10.1007/978-981-15-3992-3_23 ◽

2020 ◽

pp. 283-294

Author(s):

M. G. Bindu ◽

M. K. Sabu

Keyword(s):

Feature Selection ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

High Dimensional ◽

Centre Of Gravity ◽

Bee Colony ◽

High Dimensional Datasets

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Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

Algorithms ◽

10.3390/a14110324 ◽

2021 ◽

Vol 14 (11) ◽

pp. 324

Author(s):

Yuanzi Zhang ◽

Jing Wang ◽

Xiaolin Li ◽

Shiguo Huang ◽

Xiuli Wang

Keyword(s):

Feature Selection ◽

Artificial Bee Colony ◽

Solution Space ◽

High Dimensional ◽

Classification Error ◽

Feature Subset ◽

Abc Algorithm ◽

Bee Colony ◽

Whale Optimization ◽

High Dimensional Datasets

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.

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