Multiple criteria optimization-based data mining methods and applications: a systematic survey

摘要

Support Vector Machine, an optimization technique, is well known in the data mining community. In fact, many other optimization techniques have been effectively used in dealing with data separation and analysis. For the last 10 years, the author and his colleagues have proposed and extended a series of optimization-based classification models via Multiple Criteria Linear Programming (MCLP) and Multiple Criteria Quadratic Programming (MCQP). These methods are different from statistics, decision tree induction, and neural networks. The purpose of this paper is to review the basic concepts and frameworks of these methods and promote the research interests in the data mining community. According to the evolution of multiple criteria programming, the paper starts with the bases of MCLP. Then, it further discusses penalized MCLP, MCQP, Multiple Criteria Fuzzy Linear Programming (MCFLP), Multi-Class Multiple Criteria Programming (MCMCP), and the kernel-based Multiple Criteria Linear Program, as well as MCLP-based regression. This paper also outlines several applications of Multiple Criteria optimization-based data mining methods, such as Credit Card Risk Analysis, Classification of HIV-1 Mediated Neuronal Dendritic and Synaptic Damage, Network Intrusion Detection, Firm Bankruptcy Prediction, and VIP E-Mail Behavior Analysis.