A Feature-Based Serial Approach to Classifier Combination |
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Authors: | M Last H Bunke A Kandel |
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Affiliation: | (1) Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel, IL;(2) Institut fur Informatik and angewandte Mathematik, University of Bern, Bern, Switzerland, CH;(3) Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA, US |
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Abstract: | : A new approach to the serial multi-stage combination of classifiers is proposed. Each classifier in the sequence uses a
smaller subset of features than the subsequent classifier. The classification provided by a classifier is rejected only if
its decision is below a predefined confidence level. The approach is tested on a two-stage combination of k-Nearest Neighbour classifiers. The features to be used by the first classifier in the combination are selected by two stand-alone
algorithms (Relief and Info-Fuzzy Network, or IFN) and a hybrid method, called ‘IFN + Relief’. The feature-based approach
is shown empirically to provide a substantial decrease in the computational complexity, while maintaining the accuracy level
of a single-stage classifier or even improving it.
Received: 24 November 2000, Received in revised form: 30 November 2001, Accepted: 05 June 2002
ID="A1" Correspondence and offprint requests to: M. Last, Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel. Email:
mlast@bgumail.bgu.ac.il |
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Keywords: | : Classifier combination Decision-tree classifier Feature selection Info-Fuzzy Network (IFN) Nearest neighbour classifier Sequential combination |
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