Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, a parallel Optimal Best-First search Branch-and-Bound(B&B) algorithm(obs) is proposed and evaluated for MIN-based multiprocessor systems. The proposed algorithm decomposes a problem into a number of sub-problems and each sub-problem is processed on a small group of processors. A performance analysis is conducted to estimate the speed-up of the proposed parallel B&B algorithm. It considers both the computation and communication times to evaluate the realistic performance.Simulation data are given along with analysis results for model validation. It is shown that the proposed Optimal Best-First search B&B algorithm performs better than other reported schemes with its various advantageous features such as: less sub-problem evaluations, proper load balancing, and limited scope of remote communication through the network.