An approach to mining the multi-relational imbalanced database Export

@article{citeulike:6060736, abstract = {The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database.}, author = {Lee, C. and Tsai, C. and Wu, T. and Yang, W.}, citeulike-article-id = {6060736}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.eswa.2007.05.048}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0957417407002321}, doi = {10.1016/j.eswa.2007.05.048}, issn = {09574174}, journal = {Expert Systems with Applications}, month = {May}, number = {4}, pages = {3021--3032}, posted-at = {2009-11-03 17:02:41}, priority = {2}, title = {An approach to mining the multi-relational imbalanced database}, url = {http://dx.doi.org/10.1016/j.eswa.2007.05.048}, volume = {34}, year = {2008} }

TY - JOUR ID - citeulike:6060736 L3 - citeulike-article-id:6060736 N2 - The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multi-relational database. Nevertheless, many data nowadays such as financial transactions and medical anamneses are stored in a multi-relational database rather than a single data sheet. On the other hand, the widely used multi-relational classification approaches, such as TILDE, FOIL and CrossMine, are insensitive to handle the imbalanced databases. In this paper, we propose a multi-relational g-mean decision tree algorithm to solve the imbalanced problem in a multi-relational database. As shown in our experiments, our approach can more accurately mine a multi-relational imbalanced database. IS - 4 JF - Expert Systems with Applications SN - 09574174 EP - 3032 TI - An approach to mining the multi-relational imbalanced database VL - 34 SP - 3021 AU - Lee, C AU - Tsai, C AU - Wu, T AU - Yang, W PY - 2008/05// UR - http://dx.doi.org/10.1016/j.eswa.2007.05.048 ER -