Evaluation of classifier models using stratified tenfold cross validation techniques

Communications in Computer and Information Science

Volumn 270 CCIS, Issue PART II, 2012, Pages 680-690

  Purushotham, Swarnalatha ^a   Tripathy, B K ^a  

^a VIT UNIVERSITY   (India)

Author keywords

Accuracy; Class label; Dataset; Model deduction; Model induction; Stratified Tenfold Cross Validation; Test data; Training data

Indexed keywords

ACCURACY; CLASS LABELS; CROSS VALIDATION; DATA SETS; MODEL DEDUCTION; TEST DATA; TRAINING DATA;

FORECASTING; ROUGH SET THEORY;

INFORMATION SYSTEMS;

EID: 84865959229     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-29216-3_74     Document Type: Conference Paper

References (12)

1. Dubois, D., Prade, H.: Rough fuzzy sets model. International Journal of General Systems 46(1), 191-208 (1990)
2. Kibler, D., Langley, P.: Machine learning as an experimental science. In: Proc. of 1988 Euro. Working Session on Learning, pp. 81-92 (1988)
3. Wolpert, D.: On the connection between insample testing and generalization error. Complex Systems 6, 47-94 (1992)
4. Written, I.H., Frank, E.: Data Mining -Practical Machine Learning Tools and Techniques With Java Implementations, p. 371
5. Gascuel, O., Caraux, G.: Statistical significance in inductive learning. In: Proc. of the European Conf. on Artificial Intelligence (ECAI), New York, pp. 435-439 (1992)
6. Pawlak, Z.: Rough sets. International Jour. of Information and Computer Science 11, 341-356 (1982)
7. Maji, P., Pal, S.K.: Rough Set Based Generalized Fuzzy C-Means Algorithm and Quantitative Indices. IEEE Transactions on Systems, Man, and Cybernetics-Part B, Cybernetics 37(6), 1529-1540 (2007)
8. Murphy, P.M.: UCI repository of machine learning databases - a machinereadable data repository. Maintained at the Department of Information and Computer Science, University of California, Irvine (1995), Anonymous FTP from, ftp.ics.uci.edu. inthedirectorypub/machine-learning-databases
9. Kohavi, R.: Computer Science Department, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Stanford University, Stanford, CA 94305, ronnyKGCS,Stanford EDU, KOHAVI, pp. 1137-1143
10. Dietteric, T.G.: Department of Computer Science, Oregon State University, Approximate Statistical Tests for comparing Supervised Classification Learning Algorithms, Corvaellis OR 9733, December 30 (1997)
11. Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)
12. Zadeh, L.A.: Fuzzy Sets. Information and Control 11, 338-353 (1965)