English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 59161/92571 (64%)
造访人次 : 746065      在线人数 : 55
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118997


    题名: Cost-sensitive decision tree with multiple resource constraints
    作者: Wu, Chia-Chi;Chen, Yen-Liang;Tang, Kwei
    关键词: Data mining;Machine Learning;Decision tree;Cost-sensitive learning
    日期: 2019-10
    上传时间: 2020-07-28 12:10:37 (UTC+8)
    出版者: Springer New York LLC
    摘要: 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.
    關聯: Applied Intelligence 49, p.3765-3782
    显示于类别:[管理科學學系暨研究所] 期刊論文

    文件中的档案:

    档案 大小格式浏览次数
    index.html0KbHTML21检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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