An unsupervised feature selection algorithm based on ant colony optimization

This paper presents a novel hybrid feature selection algorithm based on Ant Colony Optimization (ACO) and Probabilistic Neural Networks (PNN). The wavelet packet transform (WPT) was used to process the bearing vibration signals and to generate vibration signal features. Then the hybrid feature selection algorithm was used to select the most relevant features for diagnostic purpose. Experimental results for bearing fault diagnosis have shown that the proposed hybrid feature selection method has greatly improved the diagnostic performance.

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A new feature selection algorithm based on binary ant colony optimization

The 5th Conference on Information and Knowledge Technology ◽

10.1109/ikt.2013.6620037 ◽

2013 ◽

Cited By ~ 18

Author(s):

Shima Kashef ◽

Hossein Nezamabadi-pour

Keyword(s):

Feature Selection ◽

Ant Colony Optimization ◽

Ant Colony ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

New Feature

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MLACO: A multi-label feature selection algorithm based on ant colony optimization

Knowledge-Based Systems ◽

10.1016/j.knosys.2019.105285 ◽

2020 ◽

Vol 192 ◽

pp. 105285 ◽

Cited By ~ 7

Author(s):

Mohsen Paniri ◽

Mohammad Bagher Dowlatshahi ◽

Hossein Nezamabadi-pour

Keyword(s):

Feature Selection ◽

Ant Colony Optimization ◽

Ant Colony ◽

Selection Algorithm ◽

Feature Selection Algorithm

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An Improved Feature Selection Algorithm Based on Ant Colony Optimization

IEEE Access ◽

10.1109/access.2018.2879583 ◽

2018 ◽

Vol 6 ◽

pp. 69203-69209 ◽

Cited By ~ 15

Author(s):

Huijun Peng ◽

Chun Ying ◽

Shuhua Tan ◽

Bing Hu ◽

Zhixin Sun

Keyword(s):

Feature Selection ◽

Ant Colony Optimization ◽

Ant Colony ◽

Selection Algorithm ◽

Feature Selection Algorithm

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An improved feature selection algorithm based on graph clustering and ant colony optimization

Knowledge-Based Systems ◽

10.1016/j.knosys.2018.06.025 ◽

2018 ◽

Vol 159 ◽

pp. 270-285 ◽

Cited By ~ 28

Author(s):

Hojat Ghimatgar ◽

Kamran Kazemi ◽

Mohamamd Sadegh Helfroush ◽

Ardalan Aarabi

Keyword(s):

Feature Selection ◽

Ant Colony Optimization ◽

Graph Clustering ◽

Ant Colony ◽

Selection Algorithm ◽

Feature Selection Algorithm

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A new unsupervised feature selection algorithm using similarity-based feature clustering

Computational Intelligence ◽

10.1111/coin.12192 ◽

2018 ◽

Vol 35 (1) ◽

pp. 2-22 ◽

Cited By ~ 3

Author(s):

Xiaoyan Zhu ◽

Yu Wang ◽

Yingbin Li ◽

Yonghui Tan ◽

Guangtao Wang ◽

...

Keyword(s):

Feature Selection ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Feature Clustering ◽

Unsupervised Feature Selection

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Feature Selection Based on Ant Colony Optimization for Image Classification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.319.337 ◽

2013 ◽

Vol 319 ◽

pp. 337-342

Author(s):

Li Tu ◽

Li Zhi Yang

Keyword(s):

Feature Selection ◽

Image Classification ◽

Ant Colony Optimization ◽

Classification Accuracy ◽

Image Data ◽

Ant Colony ◽

Data Sets ◽

Classification Rules ◽

Selection Algorithm ◽

Artificial Ants

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

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