A study on the performances of dynamic classifier selection based on local accuracy estimation

Dynamic classifier selection (DCS) plays a strategic role in the field of multiple classifier systems (MCS). This paper proposes a study on the performances of DCS by Local Accuracy estimation (DCS-LA). To this end, upper bounds against which the performances can be evaluated are proposed. The experimental results on five datasets clearly show the effectiveness of the selection methods based on local accuracy estimates. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.