A selective Bayes classifier with meta-heuristics for incomplete data

Authors:
Hung-Chun Lin;Chao-Ton Su
Affiliations:
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
Venue:
Neurocomputing
Year:
2013

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Abstract

Numerous classification approaches have been proposed; however, most of them address complete data problems. Because actual data sets are typically incomplete for various reasons, algorithms for classification with incomplete data have received increasing attention and numerous methods have been developed to address incomplete data. These approaches have certain drawbacks or the pre-assumption of data missing at random, which is difficult to verify. Ramoni and Sebastiani presented the Robust Bayes Classifier (RBC) to eliminate the assumption. Nevertheless, RBC assumes that the attributes are independent for each class. A broken assumption degenerates classification performance. Therefore, to find the feature subset with the best performance is the top priority. Because selecting features belongs to NP-complete problems, this study combined Electromagnetism-like Mechanism algorithm with RBC for feature selection and classification tasks with incomplete data. A numerical experiment on 11 incomplete data sets was conducted. The results indicated greatly improved RBC performance combined with each feature selection approach. The proposed hybrid method outperformed the other algorithms not only in balanced classification accuracy, but also in efficiency of feature selection.