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
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;
Conference_Titel :
Cognitive Informatics & Cognitive Computing (ICCI*CC), 2014 IEEE 13th International Conference on
Conference_Location :
London
Print_ISBN :
978-1-4799-6080-4
DOI :
10.1109/ICCI-CC.2014.6921492