In biomedical researches a massive amount of data are produced day after day, using machine learning algorithms to discover the knowledge is very important in early diagnosis, prevention and treatment, as well as drug development. Biomedical data like DNA microarray suffers from curse of dimensionality phenomenon, since there are a huge number of features (genes) with high ambiguity. Feature selection is still a hot topic which cares about reducing the high of dimensionality by applying different techniques. Different contributions are conducted with new models, frameworks, methodologies and algorithms aiming to dissolve the curse of dimensionality problem and produce more meaningful and reliable data. The objective of this study is to explain the concept of feature selection, its methods, the algorithms and techniques that have been recently used in microarray data and the most popular microarray datasets were used. Moreover, the challenges that can appear when selecting more informative and non-redundant features from high dimensional datasets.

Feature Selection, Filter Method, Wrapper Method, Hybrid Method, DNA microarray, Metaheuristic

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