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Lazy Classication with Interval Pattern Structures: Application to Credit Scoring

Pattern structures allow one to approach the knowledge extraction problem in case of arbitrary object descriptions. They provide the way to apply Formal Concept Analysis (FCA) techniques to non-binary contexts. However, in order to produce classification rules a concept lattice should be built. For non-binary contexts this procedure may take much time and resources. In order to tackle this problem, we introduce a modification of the lazy associative classification algorithm and apply it to credit scoring. The resulting quality of classification is compared to existing methods adopted in bank systems.