A Research Travelogue on Feature Sub Set Selection AlgorithmsAuthor : R. Ravikumar and M. Babu Reddy
Volume 8 No.3 Special Issue:June 2019 pp 162-164
In machine learning as the dimensionality of the data rises, the amount of data required to provide a reliable analysis grows exponentially. To perform dimensionality reduction on high-dimensional micro array data, many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. Analyzing micro arrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. Feature selection has been an active and fruitful field of research area in pattern recognition, machine learning, statistics and data mining communities. The main objective of this paper is feature selection is to choose a subset of input variables by eliminating features.
Classification, Clustering, Feature Selection, Machine Learning, SVM
 H. Liu and Z. Zhao, “Manipulating data and dimension reduction methods: Feature selection”, in Encyclopedia of Complexity and Systems Science. Berlin, Germany: Springer, pp. 5348–5359, 2009.
 H. Liu, H. Motoda, R. Setiono, and Z. Zhao, “Feature selection: An ever evolving frontier in data mining”, in Proc. JMLR Feature Sel. Data Min., Hyderabad, India, Vol. 10. pp. 4–13, 2010.
 Y. Zhai, Y. S. Ong, and I. W. Tsang, “The emerging „big dimensionality”, IEEE Comput. Intell. Mag., Vol. 9, No. 3, pp. 14–26, Aug. 2014.
 P. Praveen, and B. Rama, “A Novel Approach to Improve the Performance of Divisive Clustering-BST”, In: S. Satapathy, V. Bhateja, K. Raju, B. Janakiramaiah (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, Vol. 542, Springer, Singapore, 2018.
 V. Bolón-Canedo, N. Sánchez-Maroño and A. AlonsoBetanzos, “A review of feature selection methods on synthetic data”, Knowledge and information systems, Vol. 34, No.3, pp. 483-519, 2013.
 V. Bolón-Canedo, N. Sánchez-Maroño and A. AlonsoBetanzos, “Recent advances and emerging challenges of feature selection in the context of big data”, Knowledge and information systems, Vol. 86, pp. 33-45, Sept. 2015
 Saroj and Jyoti, “Multi-Objective Genetic Algorithm Approach to Feature Subset Optimization”, IEEE International Advance Computing Conference (IACC), 2014.
 R. Ravi Kumar, M. Babu Reddy and P. Praveen, “A review of feature subset selection on unsupervised learning”, 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, pp. 163-167, 2017. DOI: 10.1109/AEEICB.2017. 7972404, 2017
 Vasily Sachnev and Hyoung Kim, “Binary Coded Genetic Algorithm with Ensemble Classifier for Feature in JPEG Steg analysis”, IEEE Nineth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, April 2014.
 Hoai Bach Nguyen, Bing Xue, Ivy Liu and Mengjie Zhang. “Filter Based Backward Elimination in Wrapper based PSO For Feature Selection Classification”, IEEE Congress On Evolutionary Computation (CEC), July 2014.
 P. Praveen and B. Rama, “An empirical comparison of Clustering using hierarchical methods and K-means”, 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, pp. 445-449, 2016. DOI: 10.1109/AEEICB.2016. 7538328
 Shima Kashef and Hossein Nezamabadi “A new Feature Selection Algorithm based on binary ant colony optimization”, IEEE 5th conference on Information and Knowledge Technology (IKT), 2013.
 P. Praveen, C. J. Babu and B. Rama, “Big data environment for geospatial data analysis”, 2016 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, pp. 1-6, 2016. DOI: 10.1109/CESYS.2016.7889816
 P. Praveen, B. Rama and T. Sampath Kumar, “An efficient clustering algorithm of minimum Spanning Tree”, 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, pp. 131-135, 2017. DOI: 10.1109/AEEICB.2017. 7972398