Abstract
Case-based reasoning (CBR) is a problem-solving paradigm that uses past experiences to solve new problems. Nearest neighbor is a common CBR algorithm for retrieving similar cases, whose similarity function is sensitive to irrelevant attributes. Taking the relevancy of the attributes into account can reduce this sensitivity, leading to a more effective retrieval of similar cases. In this paper, statistical evaluation is used for assigning relative importance of the attributes. This approach is applied to predict business failures in Australia using financial data. The results in this study indicate it is an effective and competitive alternative to predict business failures in a comprehensible manner. This study also investigates the usefulness of non-financial data derived from auditor's and directors' reports for business failure prediction. The results suggest that the particular non-financial attributes identified are not as effective as the financial attributes in explaining business failures.
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