Improved salp swarm algorithm for feature selection

Abstract Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. This paper tries to improve the structure of basic SSA to enhance solution accuracy, reliability and convergence speed. A new control parameter, inertia weight, is added to adjust the present best solution. The new method known as improved salp swarm algorithm (ISSA) is tested in feature selection task. The ISSA algorithm is consolidated with the K-nearest neighbor classier for feature selection in which twenty-three UCI datasets are utilized to assess the performance of ISSA algorithm. The ISSA is compared with the basic SSA and four other swarm methods. The results demonstrated that the proposed method produced superior results than the other optimizers in terms of classification accuracy and feature reduction.

[1]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[2]  Salwani Abdullah,et al.  Modified great deluge for attribute reduction in rough set theory , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[3]  Li-Yeh Chuang,et al.  Improved binary PSO for feature selection using gene expression data , 2008, Comput. Biol. Chem..

[4]  Xin-She Yang,et al.  A Binary Cuckoo Search and Its Application for Feature Selection , 2014 .

[5]  S.Ruba Arockia Archana.  Optimization Algorithms for Feature Selection in Classification: A Survey , 2016 .

[6]  Richard Jensen,et al.  Combining rough and fuzzy sets for feature selection , 2004 .

[7]  Hossam Faris,et al.  Feature Selection Using Salp Swarm Algorithm with Chaos , 2018, ISMSI '18.

[8]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[9]  Fevrier Valdez.  Bio-Inspired Optimization Methods , 2015, Handbook of Computational Intelligence.

[10]  Pravat Kumar Rout,et al.  Elitism based Multi-Objective Differential Evolution for feature selection: A filter approach with an efficient redundancy measure , 2017, J. King Saud Univ. Comput. Inf. Sci..

[11]  Jyoti,et al.  Multi-objective genetic algorithm approach to feature subset optimization , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[12]  S. Selvakumar,et al.  A survey on evolutionary techniques for feature selection , 2017, 2017 Conference on Emerging Devices and Smart Systems (ICEDSS).

[13]  Keke Gai,et al.  A Classification Algorithm Based on Ensemble Feature Selections for Imbalanced-Class Dataset , 2016, 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS).

[14]  Keke Gai,et al.  An Empirical Study on Preprocessing High-Dimensional Class-Imbalanced Data for Classification , 2015, 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems.

[15]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[16]  Hossam M. Zawbaa,et al.  Feature selection based on antlion optimization algorithm , 2015, 2015 Third World Conference on Complex Systems (WCCS).

[17]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[18]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, Journal of Intelligent Information Systems.

[19]  Hossam M. Zawbaa,et al.  Feature selection approach based on whale optimization algorithm , 2017, 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI).

[20]  Rasmita Dash,et al.  An Adaptive Harmony Search Approach for Gene Selection and Classification of High Dimensional Medical Data , 2018, J. King Saud Univ. Comput. Inf. Sci..

[21]  Crina Grosan,et al.  Feature Selection via Chaotic Antlion Optimization , 2016, PloS one.

[22]  Aboul Ella Hassanien,et al.  Binary grey wolf optimization approaches for feature selection , 2016, Neurocomputing.

[23]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[24]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[25]  Xin Yao,et al.  A Survey on Evolutionary Computation Approaches to Feature Selection , 2016, IEEE Transactions on Evolutionary Computation.

[26]  Salwani Abdullah,et al.  Fuzzy Modified Great Deluge Algorithm for Attribute Reduction , 2014, SCDM.

[27]  Salwani Abdullah,et al.  A fuzzy record-to-record travel algorithm for solving rough set attribute reduction , 2015, Int. J. Syst. Sci..