Multiple Criteria Quadratic Programming Research Articles

A cost-sensitive multi-criteria quadratic programming model for imbalanced data

Multiple Criteria Quadratic Programming (MCQP), a mathematical programming-based classification method, has been developed recently and proved to be effective and scalable. However, its performance degraded when learning from imbalanced data. This paper proposes a cost-sensitive MCQP (CS-MCQP) model by introducing the cost of misclassifications to the MCQP model. The empirical tests were designed to compare the proposed model with MCQP and a selection of classifiers on 26 imbalanced datasets from the UCI repositories. The results indicate that the CS-MCQP model not only performs better than the optimization-based models (MCQP and SVM), but also outperforms the selected classifiers, ensemble, preprocessing techniques and hybrid methods on imbalanced datasets in terms of AUC and GeoMean measures. To validate the results statistically, Student’s t test and Wilcoxon signed-rank test were conducted and show that the superiority of CS-MCQP is statistically significant with significance level 0.05. In addition, we analyze the effect of noisy, small disjunct and overlapping data properties on the proposed model and conclude that the CS-MCQP model achieves better performance on imbalanced data with overlapping feature than noisy and small disjunct data.

A FUZZY LINEAR PROGRAMMING-BASED CLASSIFICATION METHOD

Multiple criteria linear programming and multiple criteria quadratic programming classification models have been applied in some field in financial risk analysis and credit risk control such as credit cardholders' behavior analysis. In this paper, a fuzzy linear programming classification method with soft constraints and criteria was proposed based on the previous findings from other researchers. In this method, the satisfied result can be obtained through selecting constraint and criteria boundary variable di*, respectively. A general framework of this method is also constructed. Two real-life datasets, one from a major USA bank and the other from a database of KDD 99, are used to test the accurate rate of the proposed method. And the result shows the feasibility of this method.

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

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.

Comparison study of two kernel-based learning algorithms for predicting the distance range between antibody interface residues and antigen surface

A number of kernel-based machine algorithms have recently been used to study protein–protein interaction in the field of bioinformatics. In this paper we develop a kernel-based machine algorithm called multiple criteria quadratic programming (MCQP) to predict the distance range between antibody interface residues and the antigen surface in antigen–antibody complex. Antibodies bind their antigen using residues, which are part of the hypervariable loops. In this paper we explore the interaction between antibody interface residues and antigen in the study of antibody functions. The distance between the antibody’s interface residue and the antigen surface is one of the antigen–antibody binding characteristics used to observe the details of the antibody–antigen interaction surface. The results predicted by MCQP are compared with those predicted by the support vector machine (SVM). The MCQP algorithm classifies observations into distinct groups based on a number of criteria via a hyperplane. MCQP shows strong advantages for distances of 8 Å and 10 Å. However, SVM gives better results for a distance of 12 Å.

CLASSIFYING CREDIT CARD ACCOUNTS FOR BUSINESS INTELLIGENCE AND DECISION MAKING: A MULTIPLE-CRITERIA QUADRATIC PROGRAMMING APPROACH

A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong. The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management.