A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction

A case-based reasoning with the feature weights derived by analytic hierarchy process for... Case-based reasoning (CBR) is a methodology for problem solving and decision-making in complex and changing business environments. Many CBR algorithms are derivatives of the k -nearest neighbor ( k -NN) method, which has a similarity function to generate classification from stored cases. Several studies have shown that k -NN performance is highly sensitive to the definition of its similarity function. Many k -NN methods have been proposed to reduce this sensitivity by using various distance functions with feature weights. This paper proposes an analogical reasoning structure for feature weighting using a new framework called the analytic hierarchy process (AHP)-weighted k -NN algorithm. The paper also introduces AHP methodology for assigning relative importance in case indexing and retrieving. The AHP model is a methodology effective in obtaining domain knowledge from numerous experts and representing knowledge-guided indexing. The proposed AHP weighted k -NN algorithm has been shown to achieve classification accuracy higher than the pure k -NN algorithm. This approach is applied to bankruptcy prediction involves the examination of several criteria, both quantitative (financial ratios) and qualitative (non-financial variables). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Expert Systems with Applications Elsevier

A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction

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Publisher
Elsevier
Copyright
Copyright © 2002 Elsevier Science Ltd
ISSN
0957-4174
D.O.I.
10.1016/S0957-4174(02)00045-3
Publisher site
See Article on Publisher Site

Abstract

Case-based reasoning (CBR) is a methodology for problem solving and decision-making in complex and changing business environments. Many CBR algorithms are derivatives of the k -nearest neighbor ( k -NN) method, which has a similarity function to generate classification from stored cases. Several studies have shown that k -NN performance is highly sensitive to the definition of its similarity function. Many k -NN methods have been proposed to reduce this sensitivity by using various distance functions with feature weights. This paper proposes an analogical reasoning structure for feature weighting using a new framework called the analytic hierarchy process (AHP)-weighted k -NN algorithm. The paper also introduces AHP methodology for assigning relative importance in case indexing and retrieving. The AHP model is a methodology effective in obtaining domain knowledge from numerous experts and representing knowledge-guided indexing. The proposed AHP weighted k -NN algorithm has been shown to achieve classification accuracy higher than the pure k -NN algorithm. This approach is applied to bankruptcy prediction involves the examination of several criteria, both quantitative (financial ratios) and qualitative (non-financial variables).

Journal

Expert Systems with ApplicationsElsevier

Published: Oct 1, 2002

References

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