Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets

摘要

Classification on medical data raises several problems such as class imbalance, double meaning of missing data, volumetry or need of highly interpretable results. In this paper a new algorithm is proposed: MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data), a multi-objective local search algorithm that is conceived to deal with these issues all together. It is based on a new modelization as a Pittsburgh multi-objective partial classification rule mining problem, which is described in the first part of this paper. An existing dominance-based multi-objective local search (DMLS) is modified to deal with this modelization. After experimentally tuning the parameters of MOCA-I and determining which version of DMLS algorithm is the most effective, the obtained MOCA-I version is compared to several state-of-the-art classification algorithms. This comparison is realized on 10 small and middle-sized data sets of literature and 2 real data sets; MOCA-I obtains the best results on the 10 data sets and is statistically better than other approaches on the real data sets. (C) 2015 Elsevier B.V. All rights reserved.