Cost-sensitive decision tree with multiple resource constraints

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 期刊論文 > Item 987654321/118997

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118997

題名:	Cost-sensitive decision tree with multiple resource constraints
作者:	Wu, Chia-Chi;Chen, Yen-Liang;Tang, Kwei
關鍵詞:	Data mining;Machine Learning;Decision tree;Cost-sensitive learning
日期:	2019-10
上傳時間:	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
顯示於類別:	[管理科學學系暨研究所] 期刊論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
index.html	0Kb	HTML	52	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章

資料載入中.....