Abstract
The Branch & Bound (B&B) algorithm is a globally optimal feature selection method. The high computational complexity of this algorithm is a well-known problem. The B&B algorithm constructs a search tree, and then searches for the optimal feature subset in the tree. Previous work on the B&B algorithm was focused on how to simplify the search tree in order to reduce the search complexity. Several improvements have already existed. A detailed analysis of basic B&B algorithm and existing improvements is given under a common framework in which all the algorithms are compared. Based on this analysis, an improved B&B algorithm, BBPP+, is proposed. Experimental comparison shows that BBPP+ performs best.
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Wang, Z., Yang, J., Li, G. (2003). An Improved Branch & Bound Algorithm in Feature Selection. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_90
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DOI: https://doi.org/10.1007/3-540-39205-X_90
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