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

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).