Abstract
Nowadays, huge amounts of data are generated by many fields such as health care, astronomy, social media, sensors, and so on. When working with such data, there is a need for the removal of irrelevant, redundant, or unrelated data. Among various preprocessing techniques, dimensionality reduction is one such technique used to clean data. It helps the classifiers by reducing training time and improving the classification accuracies. In this work, the most widely used feature selection techniques were analyzed in machine learning for improving the classification as well as prediction accuracies.
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Karunakaran, V., Rajasekar, V., Joseph, S.I.T. (2021). Exploring a Filter and Wrapper Feature Selection Techniques in Machine Learning. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_40
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