Single Sequence Fast Feature Selection for High-Dimensional Data

As the first main contribution, this work proposes a feature selection algorithm to be used as base driver for comparisons in fast feature selection experiments. This heuristic algorithm tries to eliminate the redundant and irrelevant features of the datasets by creating a univariate ranking, in decreasing order with respect to their individual performance, followed by a sequential selection to establish the final set. Secondly, it presents examples where feature selection surpasses the predictive power of classifier ensembles based on feature selection. The proposed algorithm is compared to two ensemble methods, one fast feature selection algorithm, one pure ranking method and one classifier algorithm without feature selection, achieving a better performance in 17 of a total of 20 microarray gene datasets.