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Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model. In typical cross-validation, the training and validation sets must cross-over in successive rounds such that each data point has a chance of being validated against. The basic form of cross-validation is k-fold cross-validation. Other forms of cross-validation are special cases of k-fold cross-validation or involve repeated rounds of k-fold cross-validation.
In k-fold cross-validation, the data is first partitioned into k equally (or nearly equally) sized segments or folds. Subsequently k iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining k − 1 folds are used for learning. Fig. 1 demonstrates an example with k= 3. The darker section of the...
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Bouckaert R.R. Choosing between two learning algorithms based on calibrated tests. In Proc. 20th Int. Conf. on Machine Learning, 2003, pp. 51–58.
Dietterich T.G. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput., 10(7):1895–1923, 1998.
Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc., 78:316–331,1983.
Geisser S. The predictive sample reuse method with applications. J. Am. Stat. Assoc., 70(350):320–328,1975.
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. 14th Int. Joint Conf. on AI, 1995, pp. 1137–1145.
Larson S. The shrinkage of the coefficient of multiple correlation. J. Educat. Psychol., 22:45–55, 1931.
Liu H. and Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng., 17(4):491–502, 2005.
Mosteller F. and Tukey J.W. Data analysis, including statistics. In Handbook of Social Psychology. Addison-Wesley, Reading, MA, 1968.
Mosteller F. and Wallace D.L. Inference in an authorship problem. J. Am. Stat. Assoc., 58:275–309, 1963.
Refaeilzadeh P., Tang L., and Liu H. On comparison of feature selection algorithms. In Proc. AAAI-07 Workshop on Evaluation Methods in Machine Learing II. 2007, pp. 34–39.
Salzberg S. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min. Knowl. Disc., 1(3):317–328, 1997.
Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc., 36(2):111–147, 1974.
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Refaeilzadeh, P., Tang, L., Liu, H. (2009). Cross-Validation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_565
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