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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/8856

Title: An Approach to Mining the Multi-relational Imbalanced Database
Authors: Chien-I Lee;Cheng-Jung Tsai;Tong-Qin Wu;Wei-Pang Yang
Contributors: 數學系
Keywords: Data mining;Classification;Imbalance;Relational database
Date: 2008-05
Issue Date: 2011-05-10T06:28:56Z
Publisher: Elsevier Science
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
Relation: Expert Systems with Applications, 34(4):3021-3032
Appears in Collections:[數學系] 期刊論文

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