Journal
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Volume 29, Issue 3-4, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218213020600040
Keywords
Multiple classifier systems; fusion; selection; diversity; centrality measures
Categories
Funding
- Coordination for the Improvement of Higher Education Personnel (CAPES) [88881.131663/2016-01]
- Brazilian National Council for Scientific and Technological Development (CNPq) [306684/2018-2]
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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 U 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.
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