A stereo remote sensing feature selection method based on artificial bee colony algorithm

2014 ◽  
Author(s):  
Yiming Yan ◽  
Pigang Liu ◽  
Ye Zhang ◽  
Nan Su ◽  
Shu Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Selection Method ◽  
Bee Colony

Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

2020 ◽  
Author(s):  
Esra Sarac Essiz ◽  
Murat Oturakci
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Traditional Methods ◽  
Bee Colony ◽  
Nature Inspired Algorithm

Abstract As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

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.

Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

2019 ◽  
Vol 137 ◽  
pp. 46-58 ◽  
Author(s):  
Yong Zhang ◽  
Shi Cheng ◽  
Yuhui Shi ◽  
Dun-wei Gong ◽  
Xinchao Zhao
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bee Colony
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