English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6507/11669
Visitors : 30006841      Online Users : 392
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/8862

Title: A Multivariate Decision Tree Algorithm to Mine Imbalanced Data
Authors: Tsai, Cheng-Jung;Lee, Chien-I Lee;Chen, Chiu-Ting;Yang, Wei-Pang
Contributors: 數學系
Keywords: Classification;Data mining;Decision tree;Imbalance;Multivate test
Date: 2007-01
Issue Date: 2011-05-10T06:29:32Z
Publisher: World Scientific and Engineering Academy and Society (WSEAS)
Abstract: The class imbalance problem is an important issue in classification of Data mining. Among the proposed approaches, some of them modify the class distribution of the original data which would worsen the computational burden or might throw away some userful information; some are limited to specific dataset or only applicable to the dataset with numeric attribute; some would take a lot of training time due to the natural property of core techniques such as neural network; and some suffer from determining a proper threshold while the user is unfamiliar with the domain knowledge. In this paper, we proposed the HIerarchical Shrinking decision Tree (HIS-Tree) algorithm to solve these problems. HIS-Tree uses the multivariae test derived from geometric mean measurement as splitting criteria to group minority examples together. By this way, HIS-Tree can avoid discovering rules dominated by the majority examples. Finally, as shown in the experiment, HIS-Tree can predict minority/interesting examples more accurately.
Relation: WSEAS Transactions on Information Science and applications, 4(1):50-58
Appears in Collections:[數學系] 期刊論文

Files in This Item:

There are no files associated with this item.

All items in NCUEIR are protected by copyright, with all rights reserved.


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback