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Induction of “In‐between” Classes: Learning vague concepts

Moustakis Vasilis, George Potamias

Πλήρης Εγγραφή

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Περίληψη

This paper presents a synergistic iterative process, SIR, for resolving between classes assigned to cases.The "vagueness" of concepts, represented by multi-class assignment to cases, is a common phenomenon in the contextof concept learning from examples (CLFE) paradigm. The causes could be attributed to the specifics of the applicationdomain, to the poor initial representation or, to the learning heuristics themselves. The methodology presented in thispaper take advantage of multi-class assignment in order to improve learning results. Our methodology implements atwo-step iterative process: (1) an inductive algorithm runs on the training set of cases, and (2) application of a speciallydevised set of heuristics aiming to invent new classes, and resolve the conflict presented by multi-class assignment.Thus, "vague" concepts, laying "in-between" the underlying concepts, are learned. Experiments on real-world domainsfrom medicine and finance are presented, and the utility of the SIR process in decision-making tasks is discussed.

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