MeLiF: Filter Ensemble Learning Algorithm for Gene Selection

Feature filtering algorithms are commonly used for prepossessing high-dimensional datasets such as DNA microarrays. Feature selection ensemble learning is a developing field, where new efficient and robust feature selection algorithms are produced by combining existing ones. We propose a new algorithm called MeLiF which is based on ranking filters ensemble via learning linear form. We compare this algorithm on nine DNA-microarray datasets with basic feature filtering algorithms and RelifF. MeLiF has shown the best AUC score and competitive stability in comparison with other methods.