Abstract : Given the prevalence of extremely complex collections of data, feature selection—one of the essential challenges of machine learning—has received greater emphasis. It shows the vital aspects of a specific problem. Traditionally, it has been used to solve many different issues in fields such as banking (e.g., detecting fraud), surveillance systems, healthcare institutions (e.g., cancer detection), and biological data processing. It helps to get rid of all irrelevant variables and boosts classifier efficiency and precision. Initially, the model's construction should be made simpler by having fewer parameters, taking less time to train, improving applicability to prevent excessive filling, and staying away from the curse known as dimensionality. Filter methods have significance over feature selection since they can be used with any kind of machine-learning model and significantly shorten the time it takes for machine-learning algorithms to run. The assessments are intended to review the operation of various filter methods, evaluate how effectively they perform in terms of run time and estimated efficiency, and offer recommendations for usage.
Keywords : Feature selection, Filter method, Variance Threshold, SelectKBest, Information Gain, Select Percentile.
Cite : Kumar, A., & Gouri, M. H. (2023). An Approach To Using Feature Selection For Classification And Regression Dilemma Through Filter Method (1st ed., p. 57). Noble Science Press. https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-08
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