Retrieval of a set of cases similar to a new case is a problem common to a number of machine learning approaches such as nearest neighbor algorithms, conceptual clustering, and case based reasoning. A limitation of most case retrieval algorithms is their lack of attention to information acquisition costs. When information acquisition costs are considered, cost reduction is hampered by the practice of separating concept formation and retrieval strategy formation. To demonstrate the above claim, we examine two approaches. The first approach separates concept formation and retrieval strategy formation. To form a retrieval strategy in this approach, we develop the CR lc (case retrieval loss criterion) algorithm that selects attributes in ascending order of expected loss. The second approach jointly optimizes concept formation and retrieval strategy formation using a cost based variant of the ID3 algorithm (ID3 c ). ID3 c builds a decision tree wherein attributes are selected using entropy reduction per unit information acquisition cost. Experiments with four data sets are described in which algorithm, attribute cost coefficient of variation, and matching threshold are factors. The experimental results demonstrate that (i) jointly optimizing concept formation and retrieval strategy formation has substantial benefits, and (ii) using cost considerations can significantly reduce information acquisition costs, even if concept formation and retrieval strategy formation are separated.
REF empirically show that separating concept formation (model building) and retrieval strategy formation (acquisition) is a design limitation for case-based systems.
Redesigning case retrieval to reduce Information acquisition costs
Information Systems Research
Computer Science