DocumentCode
116239
Title
Formal analysis of cross-validation for rule induction using probabilistic indices
Author
Tsumoto, Shusaku ; Hirano, Shoji
Author_Institution
Dept. of Med. Inf., Shimane Univ., Izumo, Japan
fYear
2014
fDate
18-20 Aug. 2014
Firstpage
416
Lastpage
423
Abstract
This paper proposes a new framework for evaluation of cross-validation methods for rule induction based on statistical indices by using incremental sampling scheme. Although incremental sampling scheme is used for incremental rule induction, this paper shows that the same idea can be used for deletion of exampling, called decremental sampling scheme. Then, we applied this technique to the cross-validation method for rules define by the propositions whose constraints were define by inequalities of accuracy and coverage. The results show that the evaluation framework gives a powerful tool for evaluation of cross-validation.
Keywords
learning (artificial intelligence); rough set theory; sampling methods; cross-validation evaluation; cross-validation methods; decremental sampling scheme; formal analysis; incremental rule induction; incremental sampling scheme; probabilistic indices; Accuracy; Educational institutions; Error analysis; Estimation; Probabilistic logic; Set theory; Training; accuracy; coverage; cross-validation; incremental sampling scheme; rule induction;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2014 IEEE 13th International Conference on
Conference_Location
London
Print_ISBN
978-1-4799-6080-4
Type
conf
DOI
10.1109/ICCI-CC.2014.6921492
Filename
6921492
Link To Document