A novel feature selection method via mining Markov blanket

Constraint-based relevant feature selection using the Markov blanket (MB) discovery in Bayesian network (BN) has attracted widespread attention in diverse data mining applications. However, several MB discovery methods have been presented to manage low- or high-dimensional data by focusing on either improving computation efficiency or boosting learning accuracy instead of considering both. This paper presents a new constraint-based algorithm for feature selection that considers the improvement and balancing of both computational efficiency and prediction accuracy, called F eature S election via Mining M arkov B lanket (FSMB). The FSMB mines the MB containing parents-children (PC) and spouses (SP) using a forward approach to induce the true positive parents-children (PC) of a given target T. The FSMB removes false-positive PC from the PC set and never considers them again. Concurrently, the FSMB finds SP of a target T through an exhaustive search from the non-parents-children set using the V-structure strategy to differentiate both true-positive PC and SP in the MB set and then use them to remove the false-positive SP. Also, the FSMB removes the non-MB descendants using the updated PC and SP set. Extensive experiments are conducted and validated on benchmark datasets for performance evaluation. The results are compared with existing algorithms, including the Incremental Association Markov Blanket (IAMB), the Max-Min Markov Blanket (MMMB), the HITON-MB, the Simultaneous Markov Blanket (STMB), the Iterative Parents-and-Children-Based Markov Blanket (IPCMB), the Balanced Markov Blanket (BAMB), the Efficient and Effective Markov Blanket (EEMB), and the Markov Blanket discovery by Feature Selection (MBFS). Experimental results show that the FSMB outperforms the existing algorithms with higher accuracy and shorter running time.