Three-step framework of feature selection for data of DNA microarray experiments

Abstract

Dimensionality reduction of attribute set is a common preprocessing step used in machine learning. This step is especially important for high-dimensional data with low-dimensional representation such as gene expression data. Feature reduction is essential in the case of mi-croarray data because most of the microarray data attributes are believed to be unrelated to observed classes. This paper proposes a three-step feature selection framework based on feature clustering, multi-criteria assessment (Borda count) and Markov blanket. The proposed framework is a filter method so it can be used with any classification algorithm. Its classification performance and selection stability were assessed. The experimental studies were performed on 10 microarray data sets. The experimental evaluation showed that the Markov blanket filter produces results comparable to state-of-art methods in terms of classification performance. However it tends to produce unstable solutions.