Efficient feature selection method using real-valued grasshopper optimization algorithm

Abstract Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods.

[1]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Vipin Kumar,et al.  Feature Selection: A literature Review , 2014, Smart Comput. Rev..

[3]  J. Anuradha,et al.  A Survey on Particle Swarm Optimization in Feature Selection , 2011 .

[4]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

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

[6]  M. Burrows,et al.  Mechanosensory-induced behavioural gregarization in the desert locust Schistocerca gregaria , 2003, Journal of Experimental Biology.

[7]  Oguz Bayat,et al.  A grasshopper optimizer approach for feature selection and optimizing SVM parameters utilizing real biomedical data sets , 2019, Neural Computing and Applications.

[8]  Banaja Mohanty,et al.  A teaching learning based optimization technique for optimal location and size of DG in distribution network , 2016 .

[9]  Karim Faez,et al.  GA-based optimal selection of PZMI features for face recognition , 2008, Appl. Math. Comput..

[10]  Hao Dong,et al.  An improved particle swarm optimization for feature selection , 2011 .

[11]  Marcel J. T. Reinders,et al.  Random subspace method for multivariate feature selection , 2006, Pattern Recognit. Lett..

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

[13]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

[14]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[15]  Reza Azmi,et al.  A hybrid GA and SA algorithms for feature selection in recognition of hand-printed Farsi characters , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[16]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[17]  Samuel H. Huang,et al.  Feature selection based on inference correlation , 2011, Intell. Data Anal..

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

[19]  Shigeo Abe,et al.  Modified backward feature selection by cross validation , 2005, ESANN.

[20]  Adel Al-Jumaily,et al.  Feature subset selection using differential evolution and a statistical repair mechanism , 2011, Expert Syst. Appl..

[21]  Pablo A. Estévez,et al.  A review of feature selection methods based on mutual information , 2013, Neural Computing and Applications.

[22]  Fuzhong Nian,et al.  An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network , 2014 .

[23]  Tao Li,et al.  A novel hybrid genetic algorithm with granular information for feature selection and optimization , 2018, Appl. Soft Comput..

[24]  Tansel Dökeroglu,et al.  Novel multiobjective TLBO algorithms for the feature subset selection problem , 2018, Neurocomputing.

[25]  Piotr A. Kowalski,et al.  Data clustering with grasshopper optimization algorithm , 2017, 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).

[26]  Stephen J. Simpson,et al.  A behavioural analysis of phase change in the desert locust , 1999 .

[27]  Samuel H. Huang.  Dimensionality Reduction in Automatic Knowledge Acquisition: A Simple Greedy Search Approach , 2003, IEEE Trans. Knowl. Data Eng..

[28]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[29]  Karim Faez,et al.  Feature Selection Using Ant Colony Optimization (ACO): A New Method and Comparative Study in the Application of Face Recognition System , 2007, ICDM.

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

[31]  Hao Chen,et al.  A Heuristic Feature Selection Approach for Text Categorization by Using Chaos Optimization and Genetic Algorithm , 2013 .

[32]  William Eberle,et al.  Genetic algorithms in feature and instance selection , 2013, Knowl. Based Syst..

[33]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[34]  Samuel H. Huang,et al.  Fractal-Based Intrinsic Dimension Estimation and Its Application in Dimensionality Reduction , 2012, IEEE Transactions on Knowledge and Data Engineering.

[35]  Bruce A. Draper,et al.  Feature selection from huge feature sets , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[36]  Hossam Faris,et al.  Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems , 2017, Knowl. Based Syst..

[37]  Richard Nock,et al.  A hybrid filter/wrapper approach of feature selection using information theory , 2002, Pattern Recognit..