A comparative study between feature selection algorithms

Abstract

In this paper, we show a comparative study between four algorithms used in features selection; these are: decision trees, entropy measure for ranking features, estimation of distribution algorithms, and the bootstrapping algorithm. Likewise, the features selection is highlighted as the most representative task in the elimination of noise, in order to improve the quality of the dataset. Subsequently, each algorithm is described in order that the reader understands its function. Then the algorithms are applied using different data sets and obtaining the results in the selection. Finally, the conclusions of this investigation are presented.