文章基本信息

标题：Multi-Objective Classification Based on Bloomy Decision Tree
本地全文：下载
作者：Masafumi Gotoh ; Yuta Choki ; Einoshin Suzuki 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2001
卷号：16
期号：2
页码：193-201
DOI：10.1527/tjsai.16.193
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper presents a novel decision-tree induction for a multi-objective data set, i.e. a data set with a multi-dimensional class. Inductive decision-tree learning is one of the frequently-used methods for a single-objective data set, i.e. a data set with a single-dimensional class. However, in a real data analysis, we usually have multiple objectives, and a classifier which explains them simultaneously would be useful. A conventional decision-tree inducer requires transformation of a multi-dimensional class into a singledimensional class, but such a transformation can considerably worsen both accuracy and readability. In order to circumvent this problem we propose a bloomy decision tree which deals with a multi-dimensional class without such transformations. A bloomy decision tree consists of a set of decision nodes each of which splits examples according to their attribute values, and a set of .ower nodes each of which decidesa dimension of the class for examples. A flower node appears not only at the fringe of a tree but also inside a tree. Our pruning is executed during tree construction, and evaluates each dimension of the class based on Cramér’s V. The proposed method has been implemented as D3-B (Decision tree in Bloom), and tested with eleven benchmark data sets in the machine learning community. The experiments showed that D3-B has higher accuracies in nine data sets than C4.5 and tied with it in the other two data sets. In terms of readability, D3-B has a smaller number of decision nodes in all data sets, and thus outperforms C4.5. Moreover, experts in agriculture evaluated bloomy decision trees, each of which is induced from an agricultural data set, and found them appropriate and interesting.
关键词：bloomy decision tree ; multi-objective classiffication ; pruning ; knowledge discovery