Methods for dynamic classifier selection
- 1 January 1999
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
In the field of pattern recognition, the concept of multiple classifier systems (MCS) was proposed as a method for the development of high-performance classification systems. At present, the common “operation” mechanism of MCS is the “combination” of classifier outputs. Recently, some researchers have pointed out the potentialities of “dynamic classifier selection” as a new operation mechanism. In a previous paper, the authors discussed the advantages of “selection-based” MCS and proposed an algorithm for dynamic classifier selection. In this paper, a theoretical framework for dynamic classifier selection is described and two methods for selecting classifiers are proposed. Reported results on the classification of different data sets show that dynamic classifier selection is an effective method for the development of MCKeywords
This publication has 12 references indexed in Scilit:
- On combining classifiersPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Combination of multiple classifiers using local accuracy estimatesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Modularity, Combining and Artificial Neural NetsConnection Science, 1997
- On Combining Artificial Neural NetsConnection Science, 1996
- Engineering Multiversion Neural-Net SystemsNeural Computation, 1996
- THE COMBINATION OF MULTIPLE CLASSIFIERS BY A NEURAL NETWORK APPROACHInternational Journal of Pattern Recognition and Artificial Intelligence, 1995
- A method of combining multiple experts for the recognition of unconstrained handwritten numeralsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1995
- Decision combination in multiple classifier systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Methods of combining multiple classifiers and their applications to handwriting recognitionIEEE Transactions on Systems, Man, and Cybernetics, 1992
- Neural network ensemblesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990