淡江大學機構典藏:Item 987654321/118997
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 59568/92818 (64%)
Visitors : 815817      Online Users : 32
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/987654321/118997


    Title: Cost-sensitive decision tree with multiple resource constraints
    Authors: Wu, Chia-Chi;Chen, Yen-Liang;Tang, Kwei
    Keywords: Data mining;Machine Learning;Decision tree;Cost-sensitive learning
    Date: 2019-10
    Issue Date: 2020-07-28 12:10:37 (UTC+8)
    Publisher: Springer New York LLC
    Abstract: Measuring an attribute may consume several types of resources. For example, a blood test has a cost and needs to wait for a result. Resource constraints are often imposed on a classification task. In medical diagnosis and marketing campaigns, it is common to have a deadline and budget for finishing the task. The objective of this paper is to develop an algorithm for inducing a classification tree with minimal misclassification cost under multiple resource constraints. To our best knowledge, the problem has not been studied in the literature. To address this problem, we propose an innovative algorithm, namely, the Cost-Sensitive Associative Tree (CAT) algorithm. Essentially, the algorithm first extracts and retains association classification rules from the training data which satisfy resource constraints, and then uses the rules to construct the final decision tree. The approach can ensure that the classification task is done within the specified resource constraints. The experiment results show that the CAT algorithm significantly outperforms the traditional top-down approach and adapts very well to available resources.
    Relation: Applied Intelligence 49, p.3765-3782
    Appears in Collections:[Department of Management Sciences] Journal Article

    Files in This Item:

    File SizeFormat
    index.html0KbHTML24View/Open

    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