A unified view of rank-based decision combination

Date

2000-09-07

Author

Saranlı, Afşar
Demirekler, Mübeccel

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This study presents a theoretical investigation of the rank-based multiple classifier decision problem for closed-set pattern classification. The case with classifier raw outputs in the form of candidate class rankings is considered and formulated as a discrete optimization problem with the objective function being the total probability of correct decision. The problem has a global optimum solution but is of prohibitive dimensionality. We present a partitioning formalism under which this dimensionality can be reduced by incorporating our prior knowledge about the problem domain and the structure of the training data. The formalism can effectively explain a number of rank-based combination approaches successfully used in the literature one of which is discussed.

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Classifiers

URI

https://hdl.handle.net/11511/55056

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Department of Electrical and Electronics Engineering, Conference / Seminar