Abstract
Presenting a Data Mining technique and analyzing it often involves using a data set related to the domain. In research fortunately many well-known data sets are available and widely used to check the performance of the technique being considered. Many of the subsequent sections of this book include a practical experimental comparison of the techniques described in each one as a exemplification of this process. Such comparisons require a clear bed test in order to enable the reader to be able to replicate and understand the analysis and the conclusions obtained. First we provide an insight of the data sets used to study the algorithms presented as representative in each section in Sect. 2.1. In this section we elaborate on the data sets used in the rest of the book indicating their characteristics, sources and availability. We also delve in the partitioning procedure and how it is expected to alleviate the problematic associated to the validation of any supervised method as well as the details of the performance measures that will be used in the rest of the book. Section 2.2 takes a tour of the most common statistical techniques required in the literature to provide meaningful and correct conclusions. The steps followed to correctly use and interpret the statistical test outcome are also given.
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References
Alpaydin, E.: Introduction to Machine Learning, 2nd edn. MIT Press, Cambridge (2010)
Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recognit. 36(3), 849–851 (2003)
Ben-David, A.: A lot of randomness is hiding in accuracy. Eng. Appl. Artif. Intell. 20(7), 875–885 (2007)
Děmsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Efron, B., Gong, G.: A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Stat. 37(1), 36–48 (1983)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)
García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)
García, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)
Hochberg, Y.: A sharper bonferroni procedure for multiple tests of significance. Biometrika 75(4), 800–802 (1988)
Hodges, J., Lehmann, E.: Rank methods for combination of independent experiments in analysis of variance. Ann. Math. Statist 33, 482–497 (1962)
Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299–310 (2005)
Iman, R., Davenport, J.: Approximations of the critical region of the Friedman statistic. Commun. Stat. 9, 571–595 (1980)
Koch, G.: The use of non-parametric methods in the statistical analysis of a complex split plot experiment. Biometrics 26(1), 105–128 (1970)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on Artificial intelligence. IJCAI’95, vol. 2, pp. 1137–1143. Morgan Kaufmann Publishers Inc., San Francisco, CA (1995)
Landgrebe, T.C., Duin, R.P.: Efficient multiclass ROC approximation by decomposition via confusion matrix perturbation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 810–822 (2008)
Lim, T.S., Loh, W.Y., Shih, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)
Luengo, J., García, S., Herrera, F.: A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. Expert Syst. Appl. 36(4), 7798–7808 (2009)
Moreno-Torres, J.G., Sáez, J.A., Herrera, F.: Study on the impact of partition-induced dataset shift on k -fold cross-validation. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1304–1312 (2012)
Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Min. Knowl. Discov. 1(3), 317–328 (1997)
Shaffer, J.P.: Multiple hypothesis testing. Annu. Rev. Psychol. 46(1), 561–584 (1995)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, Boca Raton (2007)
Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, f-score and roc: A family of discriminant measures for performance evaluation. In: A. Sattar, B.H. Kang (eds.) Australian Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 4304, pp. 1015–1021. Springer (2006).
Stone, M.: Asymptotics for and against cross-validation. Biometrika 64(1), 29–35 (1977)
Tan, K.C., Yu, Q., Ang, J.H.: A coevolutionary algorithm for rules discovery in data mining. Int. J. Syst. Sci. 37(12), 835–864 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann Publishers Inc., San Francisco (2005)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. Trans. Evol. Comp. 1(1), 67–82 (1997)
Wright, S.P.: Adjusted P-values for simultaneous inference. Biometrics 48(4), 1005–1013 (1992)
Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)
Zar, J.: Biostatistical Analysis, 4th edn. Prentice Hall, Upper Saddle River (1999)
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García, S., Luengo, J., Herrera, F. (2015). Data Sets and Proper Statistical Analysis of Data Mining Techniques. In: Data Preprocessing in Data Mining. Intelligent Systems Reference Library, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-10247-4_2
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