Selecting and Combining Classifiers Based on Centrality Measures

Selecting and Combining Classifiers Based on Centrality Measures

Assumpção Silva, Ronan and Britto, Alceu S. and Enembreck, Fabricio and Sabourin, Robert and Oliveira, Luiz S.

International Journal on Artificial Intelligence Tools 2020

Abstract : Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’s accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases.