PSO with crossover operator applied to feature selection problem in classification

In recent years, there is a large number of features in datasets used in pattern classification, which include relevant, irrelevant, and redundant features. However, irrelevant and redundant features decrease the computational time and reduce the classification performance. Feature selection is a preprocessing technique that which to choose a sub-set of relevant features to achieve a similar or even better classification performance than using all features. This paper presents two new hybrid algorithms for a feature selection called particle swarm optimization with crossover operator (denoted as PSOCO1 and PSOCO2); the algorithms are based on the integration of a particle swarm optimization (PSO) and a crossover operator (CO) of the genetic algorithms. A new relevant features vector (RFV) is introduced and used by our algorithms for execute a crossover operator between the RFV and other features vectors.  To demonstrate the effectiveness of these algorithms, we compared them with standard PSO [14], PSO4-2 [8] and HGAPSO [28] on twelve benchmark datasets. The results show that the two proposed algorithms significantly reduce the number of selected features and achieve similar or even better classification accuracy in almost all cases.

[1]  Salim Chikhi,et al.  BPSO Algorithms for Knapsack Problem , 2011, WiMo/CoNeCo.

[2]  Thuzar Hlaing,et al.  Feature Selection and Fuzzy Decision Tree for Network Intrusion Detection , 2012 .

[3]  Ahmed Al-Ani.  Ant Colony Optimization for Feature Subset Selection , 2005, WEC.

[4]  Karim Faez,et al.  An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system , 2008, Appl. Math. Comput..

[5]  Fei Liu,et al.  Multi-objective optimization of machining parameters considering energy consumption , 2013, The International Journal of Advanced Manufacturing Technology.

[6]  Siti Mariyam Shamsuddin,et al.  Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks , 2011 .

[7]  Agus Harjoko,et al.  Feature Selection of the Combination of Porous Trabecular with Anthropometric Features for Osteoporosis Screening , 2015 .

[8]  Li-Yeh Chuang,et al.  Boolean binary particle swarm optimization for feature selection , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[9]  Kun-Huang Chen,et al.  An improved particle swarm optimization for feature selection , 2011, Intell. Data Anal..

[10]  Adel M. Alimi,et al.  Distributed MOPSO with a new population subdivision technique for the feature selection , 2011, 2011 5th International Symposium on Computational Intelligence and Intelligent Informatics (ISCIII).

[11]  H. Zheng,et al.  Feature selection for high dimensional data in astronomy , 2007, 0709.0138.

[12]  Li-Yeh Chuang,et al.  Improved binary particle swarm optimization using catfish effect for feature selection , 2011, Expert Syst. Appl..

[13]  Zne-Jung Lee,et al.  Parameter determination of support vector machine and feature selection using simulated annealing approach , 2008, Appl. Soft Comput..

[14]  Bin Hu,et al.  Hybrid Feature Selection Based on Improved Genetic Algorithm , 2013 .

[15]  Shengxiang Yang,et al.  Triggered Memory-Based Swarm Optimization in Dynamic Environments , 2007, EvoWorkshops.

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

[17]  Jon Atli Benediktsson,et al.  Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization , 2015, IEEE Geoscience and Remote Sensing Letters.

[18]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

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

[20]  Hui-Hua Yang,et al.  Ant colony optimization based network intrusion feature selection and detection , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[21]  Can Isik,et al.  Feature subset selection for blood pressure classification using orthogonal forward selection , 2003, 2003 IEEE 29th Annual Proceedings of Bioengineering Conference.

[22]  Alper Ekrem Murat,et al.  A discrete particle swarm optimization method for feature selection in binary classification problems , 2010, Eur. J. Oper. Res..

[23]  Mengjie Zhang,et al.  Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms , 2014, Appl. Soft Comput..

[24]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[25]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[26]  Mark Johnston,et al.  Particle Swarm Optimization based Adaboost for face detection , 2009, 2009 IEEE Congress on Evolutionary Computation.

[27]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).