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Contrast mining in large class imbalance data

Class imbalance data, in which the classes are not equally represented and the minority classes include a much smaller number of examples than other classes, is pervasive and ubiquitous, particularly in applications such as fraud/intrusion detection, medical diagnosis/monitoring, and risk management. The conventional classifiers tend to be overwhelmed by the large classes while ignoring the smaller classes. Typically, many of the existing solutions to the class imbalance problem are proposed at the data level, and a few at the algorithmic level. However, the prior methods have more or less limitations in anomaly detection according to our extensive experiments. Therefore, the thesis targets contrast mining to solve the problem of anomaly detection in imbalanced data from three aspects: feature construction, an effective algorithm for mining contrast patterns, and selection of optimal rule combinations through analysing rule interactions. Feature construction is one of the most important steps in contrast pattern mining, and any other data mining processes as well. The majority of feature construction methods, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Fourier Transformation, and Independent Component Analysis, usually generate new features by transforming the existing raw features into a new data space. Therefore, previous solutions have many limitations with respect to the objective of training highly accurate classifiers in class imbalance data sets. Incomprehensible features may be generated, based on the assumption that all the samples are independent, the feature set is unstable and sensitive to trivial change of the sample set,