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Rough sets based rule induction and multivariate statistical classification: A simulation study

Zopounidis Konstantinos, Doumpos, Michael

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Περίληψη

The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields. Over the past two decades several non-parametric approaches have been developed to address the classification problem, originating from several scientific fields. This paper is focused on the rough sets approach and the investigation of its performance as opposed to traditional multivariate statistical classification procedures, namely the linear discriminant analysis, the quadratic discriminant analysis and the logit analysis. For this purpose an extensive Monte Carlo simulation is conducted to examine the performance of these methods under different data conditions.

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