Uncertain decision tree inductive inference

被引:0
|
作者
Zarban, L. [1 ]
Jafari, S. [1 ]
Fakhrahmad, S. M. [1 ]
机构
[1] Shiraz Univ, Dept Comp Sci & Engn, Sch Elect & Comp Engn, Shiraz, Iran
关键词
induction; classification; decision tree learning; uncertainty; outliers;
D O I
10.1080/00207217.2011.593138
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Induction is the process of reasoning in which general rules are formulated based on limited observations of recurring phenomenal patterns. Decision tree learning is one of the most widely used and practical inductive methods, which represents the results in a tree scheme. Various decision tree algorithms have already been proposed such as CLS, ID3, Assistant C4.5, REPTree and Random Tree. These algorithms suffer from some major shortcomings. In this article, after discussing the main limitations of the existing methods, we introduce a new decision tree induction algorithm, which overcomes all the problems existing in its counterparts. The new method uses bit strings and maintains important information on them. This use of bit strings and logical operation on them causes high speed during the induction process. Therefore, it has several important features: it deals with inconsistencies in data, avoids overfitting and handles uncertainty. We also illustrate more advantages and the new features of the proposed method. The experimental results show the effectiveness of the method in comparison with other methods existing in the literature.
引用
收藏
页码:1305 / 1318
页数:14
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