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A lot of candidate features are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, it is often the case that majority of the candidate features are irrelevant or redundant to the learning task, which will deteriorate the performance of the employed learning algorithm and lead to the problem of overfitting. The learning accuracy and training speed may be significantly deteriorated by these superfluous features. So it is of fundamental importance to select the relevant and necessary features in the preprocessing step. This paper describes basic feature selection issues and current research points. Of course, a single article cannot be a complete review of all algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

RETRACTED ARTICLE: Feature selection for machine learning classification problems: a recent overview