A novel feature selection approach based on clustering algorithm

References

• Duan G, Hu W, Zhang Z. A novel multilayer data clustering framework based on feature selection and modified K-means algorithm. Int J Signal Process, Image Process Pattern Recogn. 2016;9(4):81–90. DOI:10.14257/ijsip.2016.9.4.08.
   Google Scholar
• Amezquita-Sanchez JP, Adeli H. Feature extraction and classification techniques for health monitoring of structures. Sci Iran. 2015;22(6):1931–1940.
   Web of Science ®Google Scholar
• Cheng T, Li P, Zhu S, et al. M-cluster and X-ray: two methods for multi jammer localization in wireless sensor networks. Integr Comput Aided Eng. 2014;21(1):19–34. doi: 10.3233/ICA-130445
   Web of Science ®Google Scholar
• Gonçalves N, Nikkilä J, Vigário R. Selfsupervised MRI tissue segmentation by discriminative clustering. Int J Neural Syst. 2014;24(1):1450004 (16 pages).
   PubMed Web of Science ®Google Scholar
• Saxena A, Prasad M, Gupta A, et al. A review of clustering techniques and developments. Neurocomputing. 2017;267:664–681.
   Web of Science ®Google Scholar
• MacQueen JB. Some methods for classification and analysis of multivariate observations. Proc. 5-th Symp. Mathematical Statistics and Probability. Berkeley, CA; 1967. 1:281–297.
   Google Scholar
• Green PE, Kim J, Carmone FJ. A preliminary study of optimal variable weighting in k-means clustering. J Classif. 1990;7(2):271–285.
   Web of Science ®Google Scholar
• Huang Z. Extensions to the k-means algorithms for clustering large data sets with categorical values. Data Min Knowl Disc. 1998;2:283–304.
   Web of Science ®Google Scholar
• Huang JZ, Ng MK, Rong H, et al. Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell. 2005;27(5):657–668. doi:10.1109/TPAMI.2005.95.
   PubMed Web of Science ®Google Scholar
• Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell. 1997;97:273–324.
   Web of Science ®Google Scholar
• Miller A. Subset selection in regression. London: CRC Press; 2002.
   Google Scholar
• Lashkari AE, Firouzmand M. Developing a toolbox for clinical preliminary breast cancer detection in different views of thermogram images using a set of optimal supervised classifiers. Sci Iran. 2018;25(3):1545–1560.
   Web of Science ®Google Scholar
• Lane MC, Xue B, Liu I, et al. Gaussian based particle swarm optimisation and statistical clustering for feature selection. In European conference on evolutionary computation in combinatorial optimization; 2014 Apr; Berlin, Heidelberg: Springer; 2014. p. 133–14.
   Google Scholar
• He Z. Evolutionary K-means with pair-wise constraints. Soft Comput. 2016;20(1):287–301.
   Web of Science ®Google Scholar
• Yuan F, Meng ZH, Zhangz X, et al. A new algorithm to get the initial centroids. Proceedings of the 3rd International Conference on Machine Learning and Cybernetics; 2004. p. 26–29.
   Google Scholar
• Zhang Ch, Xia Sh. K-means clustering algorithm with improved initial center. Second International Workshop on Knowledge Discovery and Data Mining (WKDD); 2009. p. 790–792.
   Google Scholar
• Nazeer KAA, Sebastian MP. Improving the accuracy and efficiency of the k-means clustering algorithm. Proceedings of the World Congr Eng. 2009;1:1–5.
   Google Scholar
• Stan C, Waltz D. Towards memory based reasoning. Commun ACM. 1986;29(12):1213–1228.
   Google Scholar
• Piramuthu S. Evaluating feature selection methods for learning in data mining applications. Eur J Oper Res. 2004;156(2):483–494. DOI:10.1016/S0377-2217(02)00911-6.
   Web of Science ®Google Scholar
• Shie JD, Chen SM. Feature subset selection based on fuzzy entropy measures for handling classification problems. Appl Intell. 2008;28(1):69–82.
   Web of Science ®Google Scholar
• Zhao S, Tsang ECC. On fuzzy approximation operators in attribute reduction with fuzzy rough sets. J Inf Sci. 2008;178(16):3163–3176.
   Web of Science ®Google Scholar
• Gheyas IA, Smith LS. Feature subset selection in large dimensionality domains. Pattern Recognit. 2010;43(1):5–13.
   Web of Science ®Google Scholar
• Nie F, Huang H, Cai X, et al. Efficient and robust feature selection via joint ℓ2, 1-norms minimization. Adv Neural Information Process Syst. 2010:1813–1821.
   Google Scholar
• Foithong S, Pingern O, Atachoo B. Feature subset selection wrapper based on mutual information and rough sets. Expert Syst Appl. 2011;39(1):574–584.
   Web of Science ®Google Scholar
• Huang H, Xie HB, Guo JY, et al. Ant colony optimization–based feature selection for surface electromyography signals classification. Comput Biol Med. 2011;42(1):30–38.
   PubMed Web of Science ®Google Scholar
• Ghamisi P, Benediktsson JA. Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci Remote Sens Lett. 2015;12(2):309–313.
   Web of Science ®Google Scholar
• Zhu GN, Hu J, Qi J, et al. An integrated feature selection and cluster analysis techniques for case-based reasoning. Eng Appl Artif Intel. 2015;39:14–22. DOI:10.1016/j.engappai.2014.11.006.
   Web of Science ®Google Scholar
• Zhu P, Zuo W, Zhang L, et al. Unsupervised feature selection by regularized self-representation. Pattern Recognit. 2015;48(2):438–446.
   Web of Science ®Google Scholar
• Venkataraman S, Sivakumar S, Selvaraj R. A novel clustering based feature subset selection framework for effective data classification. Indian J Sci Technol. 2016;9(4). doi: 10.17485/ijst/2016/v9i4/87038
   Google Scholar
• Moayedikia A, Ong KL, Boo YL, et al. Feature selection for high dimensional imbalanced class data using harmony search. Eng Appl Artif Intel. 2017;57:38–49. doi:10.1016/j.engappai.2016.10.008.
   Web of Science ®Google Scholar
• Bostani H, Sheikhan M. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft Comput. 2017;21(9):2307–2324.
   Web of Science ®Google Scholar
• Barani F, Mirhosseini M, Nezamabadi-pour H. Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Appl Intell. 2017;47(2):304–318.
   Web of Science ®Google Scholar
• Moradi P, Gholampour M. A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput. 2016;43:117–130.
   Web of Science ®Google Scholar
• Shunmugapriya P, Kanmani S. A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC hybrid). Swarm Evol Comput. 2017;36:27–36.
   Web of Science ®Google Scholar
• Mafarja MM, Mirjalili S. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing. 2017;260:302–312.
   Web of Science ®Google Scholar
• Gu S, Cheng R, Jin Y. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput. 2018;22(3):811–822.
   Web of Science ®Google Scholar
• Boutsidis C, Mahoney M, Drineas P. Unsupervised feature selection for the k-means clustering problem. NIPS. 2009:153–161.
   PubMedGoogle Scholar
• Boutsidis C, Zouzias A, Mahoney M, et al. Randomized dimensionality Reduction for k-means clustering. IEEE Trans. Inf Theory. 2015;61(2):1045–1062.
   Web of Science ®Google Scholar
• Abualigah LM, Khader AT, Al-Betar MA, et al. Feature selection with β-hill climbing search for text clustering application. 2017 Palestinian International Conference on Information and Communication Technology (PICICT); 2017 May; IEEE; 2017. p. 22–27.
   Google Scholar
• Abasi AK, Khader AT, Al-Betar MA, et al. A text feature selection technique based on binary multi-verse optimizer for text clustering. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT); 2019 Apr; IEEE. p. 1–6.
   Google Scholar
• Abasi AK, Khader AT, Al-Betar MA, et al. A novel hybrid multi-verse optimizer with K-means for text documents clustering. Neural Computing & Applications. 2020. DOI:10.1007/s00521-020-04945-0. (Published online).
   Web of Science ®Google Scholar
• Bache K, Lichman M. UCI machine learning repository; 2013. http://archive.ics.uci.edu/ml.
   Google Scholar
• Lane MC, Xue B, Liu I, et al. Particle swarm optimisation and statistical clustering for feature selection. Australasian Joint Conference on Artificial Intelligence. Cham: Springer; 2013. p. 214–220.
   Google Scholar
• Nguyen HB, Xue B, Liu I, et al. PSO and statistical clustering for feature selection: a new representation. In Asia-Pacific Conference on Simulated Evolution and Learning; 2014 Dec. Cham: Springer. p. 569–581.
   Google Scholar
• Xue B, Zhang M, Browne WN. New fitness functions in binary particle swarm optimization for feature selection.IEEE CEC 2012; 2012. p. 2145–2152.
   Google Scholar
• Xue B, Zhang M, Browne WN. Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput. 2014;18:261–276.
   Web of Science ®Google Scholar
• Gutlein M, Frank E, Hall M, et al. Large-scale attribute selection using wrappers. 2009 IEEE Symposium on Computational Intelligence and Data Mining; IEEE; 2009. p. 332–339. DOI:10.1109/CIDM.2009.4938668.
   Google Scholar
• Caruana R, Freitag D. Greedy attribute selection. Proc Eighth Int Conf Mach Learn; 2017. p. 28–36.
   Google Scholar
• Xue B, Lane MC, Liu I, et al. Dimension reduction in classification using particle swarm optimisation and statistical variable grouping information. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI); 2016 Dec; IEEE. p. 1–8.
   Google Scholar