Knowledge-incorporated Multiple Criteria Linear Programming Classifiers

Abstract

Classification is a main data mining task, which aims at predicting the class label of new input data on the basis of a set of pre-classified samples. Multiple Criteria Linear Programming (MCLP) is used as a classification method in data mining area, which can separate two or more classes by finding discriminate hyperplane. Although MCLP shows good performance in dealing with linear separable data, it is no longer applicable when facing with nonlinear separable problem. Kernel-based Multiple Criteria Linear Programming (KMCLP) model is developed to solve nonlinear separable problem. In this method, kernel function is introduced to project the data into a higher-dimensional space in which the data will have more chance to be linear separable. KMCLP performs well in some real applications. However, just as other prevalent data mining classifiers, MCLP and KMCLP learn only from training examples. In traditional machine learning area, there are also classification tasks in which data sets are classified only by prior knowledge, i.e. expert system. Some works combine the above two classification principle to overcome the defaults of each approach. In this section, we combine the prior knowledge and MCLP or KMCLP model to solve the problem when input consists of not only training example, but also prior knowledge.