A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm

Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson’s correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm.

[1]  Driss Aboutajdine,et al.  A two-stage gene selection scheme utilizing MRMR filter and GA wrapper , 2011, Knowledge and Information Systems.

[2]  Witold Pedrycz,et al.  Variational Inference-Based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data , 2019, IEEE Transactions on Industrial Electronics.

[3]  Seyed Mohammad Mirjalili,et al.  Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..

[4]  Gang Wang,et al.  A novel hybrid algorithm for feature selection , 2018, Personal and Ubiquitous Computing.

[5]  Jean Yee Hwa Yang,et al.  Gene-gene interaction filtering with ensemble of filters , 2011, BMC Bioinformatics.

[6]  Abdulhamit Subasi,et al.  Breast cancer diagnosis using GA feature selection and Rotation Forest , 2015, Neural Computing and Applications.

[7]  Chee Peng Lim,et al.  A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction , 2016, Expert Syst. Appl..

[8]  Hala Alshamlan,et al.  mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling , 2015, BioMed research international.

[9]  Majid Komeili,et al.  Local Feature Selection for Data Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bin Yang,et al.  Feature Selection Based on Modified Bat Algorithm , 2017, IEICE Trans. Inf. Syst..

[11]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[12]  Songyot Nakariyakul,et al.  High-dimensional hybrid feature selection using interaction information-guided search , 2018, Knowl. Based Syst..

[13]  Gang Wang,et al.  A novel bacterial foraging optimization algorithm for feature selection , 2017, Expert Syst. Appl..

[14]  Houbing Song,et al.  Feature selection and multiple kernel boosting framework based on PSO with mutation mechanism for hyperspectral classification , 2017, Neurocomputing.

[15]  Dana Kulic,et al.  An evaluation of classifier-specific filter measure performance for feature selection , 2015, Pattern Recognit..

[16]  Ratna Babu Chinnam,et al.  mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification , 2011, Inf. Sci..

[17]  Hongnian Yu,et al.  Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition , 2015, Expert Syst. Appl..

[18]  Jing Liu,et al.  Feature selection based on FDA and F-score for multi-class classification , 2017, Expert Syst. Appl..

[19]  Aboul Ella Hassanien,et al.  Modified cuckoo search algorithm with rough sets for feature selection , 2018, Neural Computing and Applications.

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

[21]  Mohammad Masoud Javidi,et al.  Utilizing the advantages of both global and local search strategies for finding a small subset of features in a two-stage method , 2018, Applied Intelligence.

[22]  Feiping Nie,et al.  Feature Selection via Global Redundancy Minimization , 2015, IEEE Transactions on Knowledge and Data Engineering.

[23]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[24]  João Miguel da Costa Sousa,et al.  Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients , 2013, Appl. Soft Comput..

[25]  Athanasios V. Vasilakos,et al.  Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data , 2016, IEEE Transactions on Services Computing.

[26]  Mohammed Azmi Al-Betar,et al.  A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios , 2017, Eng. Appl. Artif. Intell..

[27]  Verónica Bolón-Canedo,et al.  Fast‐mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High‐Dimensional Big Data , 2017, Int. J. Intell. Syst..

[28]  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.

[29]  Sai Wang,et al.  A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection , 2018, IEEE Access.

[30]  Kandhasamy Premalatha,et al.  Cuckoo search optimisation for feature selection in cancer classification: a new approach , 2015, Int. J. Data Min. Bioinform..

[31]  Aboul Ella Hassanien,et al.  Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines , 2017, J. Biomed. Informatics.

[32]  Daoliang Li,et al.  A two-stage feature selection method with its application , 2015, Comput. Electr. Eng..

[33]  S. C. Neoh,et al.  A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition , 2017, IEEE Transactions on Cybernetics.

[34]  Qiang Shen,et al.  Nature inspired feature selection meta-heuristics , 2015, Artificial Intelligence Review.

[35]  Li-Yeh Chuang,et al.  IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data , 2010 .

[36]  Yiu-ming Cheung,et al.  Feature Selection and Kernel Learning for Local Learning-Based Clustering , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Bin Zhu,et al.  Multi-source adaptation embedding with feature selection by exploiting correlation information , 2017, Knowl. Based Syst..

[38]  Indrajit N. Trivedi,et al.  Optimization of problems with multiple objectives using the multi-verse optimization algorithm , 2017, Knowl. Based Syst..

[39]  Zhixin Sun,et al.  An Improved Intrusion Detection Algorithm Based on GA and SVM , 2018, IEEE Access.