A Novel Embedded Feature Selection Algorithm and Its Application

Abstract:

An improved embedded min-max feature selection algorithm was proposed for the nonlinear multi-label classification problem， and in combination with the support vector machine algorithm， a heuristic algorithm was proposed for the complex combinatorial optimization problem. The efficiency and accuracy of the proposed algorithm were verified after a series of experiments conducted on steel faults diagnosis dataset.

Key words:min-max optimization;feature selection algorithm;nonlinear multi-label support vector machine