A wrapper-filter feature selection technique based on ant colony optimization

被引:131
作者
Ghosh, Manosij [1 ]
Guha, Ritam [1 ]
Sarkar, Ram [1 ]
Abraham, Ajith [2 ]
机构
[1] Jadavpur Univ, Comp Sci & Engn Dept, 188,Raja SC Mallick Rd, Kolkata 700032, W Bengal, India
[2] Machine Intelligence Res Labs MIR Labs, Sci Network Innovat & Res Excellence, Auburn, WA 98071 USA
关键词
Wrapper-filter method; Ant colony optimization; Feature selection; NIPS2003; challenge; NATURE-INSPIRED ALGORITHM; FEATURE SUBSET-SELECTION; GENE SELECTION;
D O I
10.1007/s00521-019-04171-3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ant colony optimization (ACO) is a well-explored meta-heuristic algorithm, among whose many applications feature selection (FS) is an important one. Most existing versions of ACO are either wrapper based or filter based. In this paper, we propose a wrapper-filter combination of ACO, where we introduce subset evaluation using a filter method instead of using a wrapper method to reduce computational complexity. A memory to keep the best ants and feature dimension-dependent pheromone update has also been used to perform FS in a multi-objective manner. Our proposed approach has been evaluated on various real-life datasets, taken from UCI Machine Learning repository and NIPS2003 FS challenge, using K-nearest neighbors and multi-layer perceptron classifiers. The experimental outcomes have been compared to some popular FS methods. The comparison of results clearly shows that our method outperforms most of the state-of-the-art algorithms used for FS. For measuring the robustness of the proposed model, it has been additionally evaluated on facial emotion recognition and microarray datasets.
引用
收藏
页码:7839 / 7857
页数:19
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