Learning classifier competence based on graph for dynamic classifier selection
Cuiqin Hou, Yingju Xia, Zhuoran Xu, Jun Sun · 2016
Classifier competence is critical important for classifier ensemble. This study proposes an optimization problem on the neighborhood graph of data and develops an iteration algorithm to learn the competences of classifiers. The learned competences of classifiers not just reflect the competitiveness of classifiers, but also vary smooth on the neighboring data. Experimental results on five different data sets show the dynamic classifier selection based classification systems with the learned classifier competence perform competitively.