A Survey on Multi-relational Classification of Imbalanced Databases

44 Hemlata Pant and Dr. Reena Srivastava A SURVEY ON MULTI-RELATIONAL CLASSIFICATION OF IMBALANCED DATABASES Hemlata Pant, Dr. Reena Srivastava Research Scholar, School of Engineering, BBD University, Lucknow Dean, School of Computer Applications, BBD University, Lucknow ____________________________________________________________________________________ ABSTRACT: The multirelational classification algorithms are designed to search for patterns across multiple interlinked relations in a relational database. For better classification these methods search for relevant features from a target relation and the relations related to it. Most of these methods are based on the assumption that the classes in the target relation are equally represented. They thus tend to produce poor predictive performance over the underrepresented class in the data. In case of imbalance database the problem of learning from imbalanced data is a relatively new challenge that has attracted growing attention from both academia and industry. The major concern in imbalanced learning problem is the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. In this paper we discuss various methods and approaches for classification of imbalanced multirelational databases.