Logical Analysis of Multiclass Data with Relaxed Patterns
نویسندگان
چکیده
This paper proposes a relaxed algorithm based on mixed integer linear programming (MILP) to extend the LAD methodology to solve multi-class classification problems, where One-vs-Rest (OvR) learning models are constructed to classify observations in predefined classes. The suggested algorithm has two control parameters, homogeneity and prevalence, for improving the classification accuracy of the generated patterns. The utility of the proposed approach is demonstrated through experiments on multi-class benchmark datasets.
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