English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58791/92483 (64%)
Visitors : 626236      Online Users : 33
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/119626

    Title: Decision tree induction with a constrained number of leaf nodes
    Authors: Wu, Chia-Chi;Chen, Yen-Liang;Liu, Yi-Hung;Yang, Xiang-Yu
    Keywords: Classification;Data mining;Decision tree;Constraint tree
    Date: 2016-04-15
    Issue Date: 2020-11-24 12:10:35 (UTC+8)
    Abstract: With the advantages of being easy to understand and efficient to compute, the decision tree method has long been one of the most popular classifiers. Decision trees constructed with existing approaches, however, tend to be huge and complex, and consequently are difficult to use in practical applications. In this study, we deal with the problem of tree complexity by allowing users to specify the number of leaf nodes, and then construct a decision tree that allows maximum classification accuracy with the given number of leaf nodes. A new algorithm, the Size Constrained Decision Tree (SCDT), is proposed with which to construct a decision tree, paying close attention on how to efficiently use the limited number of leaf nodes. Experimental results show that the SCDT method can successfully generate a simpler decision tree and offers better accuracy.
    Relation: Applied Intelligence 45(3), p.673-685
    DOI: 10.1007/s10489-016-0785-z
    Appears in Collections:[Department of Management Sciences] Journal Article

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

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