標題: A top-down and greedy method for discretization of continuous attributes
作者: Lee, Chien-, I
Tsai, Cheng-Jung
Yang, Ya-Ru
Yang, Wei-Pang
資訊工程學系
Department of Computer Science
公開日期: 2007
摘要: Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier CACC and CAIM achieve comparable results.
URI: http://dx.doi.org/10.1109/FSKD.2007.129
http://hdl.handle.net/11536/134465
ISBN: 978-0-7695-2874-8
DOI: 10.1109/FSKD.2007.129
期刊: FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 1, PROCEEDINGS
起始頁: 472
結束頁: +
Appears in Collections:Conferences Paper