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Feature Selection, Particle Swarm Optimization, Healthcare Data Classification, Wrapper Method
As a result of the rapid development of technology, data that contain a large number of features are produced from various applications such as biomedical, social media, face recognition, etc. Processing of these data is a challenging task to existing data mining and machine learning algorithms to make the decision. To reduce the size of the data for processing, a feature selection technique is needed. The feature selection is a well-known attribute selection or variable selection. The objective of the feature selection is to minimize the number of attributes contains in the dataset by eliminating the unwanted and repeated attributes to improve the classification accuracy and reduce the computation cost. Although various feature selection methods are proposed, in literature, to classify the healthcare data especially cancer diagnosis, finding an informative feature for medical datasets has still remained a challenging issue in the data mining and machine learning domain. Therefore, this paper presents a feature selection approach with the wrapper method (WFS) using particle swarm optimization (PSO) search to improve the accuracy of healthcare data classification. This work is evaluated on five benchmark medical datasets publicly available from the UCI machine learning repository. The experimental results showed that the WFS-PSO approach produces higher classification accuracy applied to different classification algorithms.
Thinzar Saw, Phyu Hnin Myint, "Feature Selection to Classify Healthcare Data using Wrapper Method with PSO Search", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.9, pp.31-37, 2019. DOI:10.5815/ijitcs.2019.09.04
Shokouhifar M, Sabet S. A hybrid approach for effective feature selection using neural networks and artificial bee colony optimization [C]. In 3rd international conference on machine vision (ICMV 2010) 2010 Dec (pp. 502-506).
Yu L, Liu H. Feature selection for high-dimensional data: A fast correlation-based filter solution [C]. In Proceedings of the 20th international conference on machine learning (ICML-03), Washington DC, 2003 (pp. 856-863).
Mafarja M, Sabar NR. Rank based binary particle swarm optimisation for feature selection in classification [C]. In Proceedings of the 2nd International Conference on Future Networks and Distributed Systems 2018 (ICFNDS’18) June 26-27, 2018, Amman, Jordan, (p. 19). ACM.“doi:10.1145/3231053.3231072”.
Uzer, M.S., Yilmaz, N. and Inan, O. Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification [J]. The Scientific World Journal, 2013. Article ID 419187, 10 pages.“doi:10.1155/2013/419187”.
Thangaraju, P. and Mehala, R. Performance analysis of PSO-KStar classifier over liver diseases [J]. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(7), 2015.
Salma, M.U., and Doreswamy. PSO based fast K-means algorithm for feature selection from high dimensional medical data set [C]. In 2016 10th International Conference on Intelligent Systems and Control (ISCO) 2016, January (pp. 1-6). IEEE.
Almayyan, W. Lymph diseases prediction using random forest and particle swarm optimization [J]. Journal of Intelligent Learning Systems and Applications, 2016, January, 8(03), p.51-62.“doi:10.4236/jilsa.2016.83005”.
Rouhi, A. and Nezamabadi-pour, H. A hybrid feature selection approach based on ensemble method for high-dimensional data [C]. In 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) (pp. 16-20) March, 2017. IEEE.
Rouhi, A. and Nezamabadi-pour, H. Filter-based feature selection for microarray data using improved binary gravitational search algorithm [C]. In 2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) (pp. 1-6). March, 2018. IEEE.
Gao, L., Ye, M. and Wu, C. Cancer classification based on support vector machine optimized by particle swarm optimization and artificial bee colony [J]. Molecules, 2017, 22(12), p.2086.
D.sheela Jeyarani, Dr.mrs.a.pethalakshmi, and Dr.mrs.k. Jayapriya. Optimal feature selection algorithm for high dimensional data sets using particle swarm optimization [J]. International Journal of Latest Trends in Engineering and Technology (IJLTET), March, 2017, Vol-8, Issue (2) p.200-211. “doi.org/10.21172/1.82.028”.
Chen, Yiyuan et al. An effective feature selection scheme for healthcare data classification using binary particle swarm optimization [C]. 2018 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2018. “doi:10.1109/ITME.2018.00160”.
Pradana, A.C. and Aditsania, A. Implementing binary particle swarm optimization and C4.5 decision tree for cancer detection based on microarray data classification [J]. In Journal of Physics: Conference Series 2019, March (Vol. 1192, No. 1, p. 012014). IOP Publishing. “doi:10.1088/1742-6596/1192/1/012014”.
Bajeh, Amos O., Bukola O. Funso, and Fatima E. Usman-Hamza. Performance Analysis of Particle Swarm Optimization for Feature Selection [J]. FUOYE Journal of Engineering and Technology 4.1 (2019).
UCI Machine Learning Repository: Available online: http://archive.ics.uci.edu/ml/. Irvine, CA: University of California, School of Information and Computer Science.
WEKA: Data Mining and Machine Learning Software. Available online: http://www.cs.waikato.ac.nz/ml/weka/
Kothari, V., Anuradha, J., Shah, S. and Mittal, P. A survey on particle swarm optimization in feature selection [C]. In International Conference on Computing and Communication Systems (pp. 192-201). 2011, December, Springer, Berlin, Heidelberg.
Hall, M.A. Correlation-based feature selection for machine learning. (Thesis) April, 1999.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection [J]. In IJCAI 1995, August ,(Vol. 14, No. 2, pp. 1137-1145).