標題: A new probabilistic induction method
作者: Hou, RH
Hong, TP
Tseng, SS
Kuo, SY
資訊工程學系
Department of Computer Science
關鍵字: adjusted residual;induction;probabilistic;relevant pattern
公開日期: 1-二月-1997
摘要: Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.
URI: http://hdl.handle.net/11536/754
ISSN: 0168-7433
期刊: JOURNAL OF AUTOMATED REASONING
Volume: 18
Issue: 1
起始頁: 5
結束頁: 24
顯示於類別:期刊論文


文件中的檔案:

  1. A1997WK93400002.pdf