Article ID: | iaor201527845 |
Volume: | 248 |
Issue: | 2 |
Start Page Number: | 593 |
End Page Number: | 606 |
Publication Date: | Jan 2016 |
Journal: | European Journal of Operational Research |
Authors: | Reiners Torsten, Caserta Marco |
Keywords: | optimization, datamining, heuristics: genetic algorithms |
In this paper, we address the binary classification problem, in which one is given a set of observations, characterized by a number of (binary and non‐binary) attributes and wants to determine which class each observation belongs to. The proposed classification algorithm is based on the Logical Analysis of Data (LAD) technique and belongs to the class of supervised learning algorithms. We introduce a novel metaheuristic‐based approach for pattern generation within LAD. The key idea relies on the generation of a pool of patterns for each given observation of the training set. Such a pool is built with one or more criteria in mind (