DOI: 10.3724/SP.J.1004.2008.00298

Acta Automatica Sinica (自动化学报) 2008/34:3 PP.298-304

Generalized Rough Set Method for Ensemble Feature Selection and Multiple Classifier Fusion

For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set. In the paper, the relative dominance decision reduct (RDDR) with respect to multiple decision tables is presented to obtain the best feature subsets and interclass separability from different feature spaces. Then, the ensemble attribute reduction (EAR) method is proposed for ensemble feature selection. Using the KD-DWV algorithm based on knowledge discovery, the effectiveness of EAR was examined with the vegetation classification on a hyperspectral image. The result of the comparison experiment shows that EAR can be used to improve the generalization of multiple classifier system by combining appropriate multiple classifier fusion algorithm.

Key words:Ensemble feature selection, multiple classifier fusion, generalized rough set, hyperspectral

ReleaseDate:2014-07-21 14:25:59

Funds:Supported by National Natural Science Foundation of China (60574033), National Basic Research Program of China (973 Program)

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