An approach to mining the multi-relational imbalanced database

Chien I. Lee, Cheng Jung Tsai, Tong Qin Wu, Wei Pang Yang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3021-3032
Number of pages12
JournalExpert Systems with Applications
Issue number4
Publication statusPublished - 2008 May 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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