Conditional Mutual Information Based Feature Selection for Classification Task [chapter]

Jana Novovičová, Petr Somol, Michal Haindl, Pavel Pudil
Lecture Notes in Computer Science  
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
more » ... 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.
doi:10.1007/978-3-540-76725-1_44 dblp:conf/ciarp/NovovicovaSHP07 fatcat:yksosewbnvgh7gztabk3vj2guq