We propose a sequential forward feature selection method to find a subset of features that are most relevant to the classification task. Our approach uses novel estimation of the conditional mutual information between candidate feature and classes, given a subset of already selected features which is utilized as a classifier independent criterion for evaluation of feature subsets. The proposed mMIFS-U algorithm is applied to text classification problem and compared with MIFS method and MIFS-U method proposed by Battiti and Kwak and Choi, respectively. Our feature selection algorithm outperforms MIFS method and MIFS-U in experiments on high dimensional Reuters textual data. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Novovičová, J., Somol, P., Haindl, M., & Pudil, P. (2007). Conditional mutual information based feature selection for classification task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4756 LNCS, pp. 417–426). Springer Verlag. https://doi.org/10.1007/978-3-540-76725-1_44
Mendeley helps you to discover research relevant for your work.