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Boundary expansion algorithm of a decision tree inductionfor an imbalanced dataset |
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| รหัสดีโอไอ | |
| Creator | 1. Kesinee Boonchuay 2. Krung Sinapiromsaran 3. Chidchanok Lursinsap |
| Title | Boundary expansion algorithm of a decision tree inductionfor an imbalanced dataset |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2560 |
| Journal Title | Songklanakarin Journal of Science and Technology (SJST) |
| Journal Vol. | 39 |
| Journal No. | 5 |
| Page no. | 665 |
| Keyword | C4.5,decision tree,classification,boundary expansion algorithm |
| ISSN | 0125-3395 |
| Abstract | A decision tree is one of the famous classifiers based on a recursive partitioning algorithm. This paper introduces theBoundary Expansion Algorithm (BEA) to improve a decision tree induction that deals with an imbalanced dataset. BEA utilizesall attributes to define non-splittable ranges. The computed means of all attributes for minority instances are used to findthe nearest minority instance, which will be expanded along all attributes to cover a minority region. As a result, BEA cansuccessfully cope with an imbalanced dataset comparing with C4.5, Gini, asymmetric entropy, top-down tree, and Hellingerdistance decision tree on 25 imbalanced datasets from the UCI Repository. |
