Exceptional Phenomena Knowledge Discovery by Information Granulation and Statistical Learning Theories

Abstract

The learning logic of exceptions is a considerable challenge in data mining and knowledge discovery. Exceptions are the rare data which
are adhered from unusual positive behavior patterns. This is important to promote confidence to a limited number of records for effective
learning of abnormality. In this study, a new synthetic approach based on statistical learning theory and information granulation theory is
presented for confidence improvement of exceptional data learning. The proposed method follows under sampling approach for exceptional
data detection. Information granulation theory is used for granules creation from data consecutively. Then, the support vector machine is
applied to each granule. Exceptions and normal data is separated based on data point distance distribution from support vectors. The
knowledge of normal and abnormal behavior has been extracted by a new method as a bottom-up learning approach. Efficiency of the
proposed model has been determined by applying it to the Iran stock market data for abnormal stock selection. The superiority of the
obtained results toward the outcome of applying decision tree, traditional SVM and neural network is considerable. Accuracy of proposed
method was measured by g-means index. The outcomes show the capability of proposed approach in abnormality detection and exceptional
behavior learning.