Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining

Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for anal...

[1]  Ethem Alpaydin,et al.  Incremental construction of classifier and discriminant ensembles , 2009, Inf. Sci..

[2]  Jaume Bacardit,et al.  Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System , 2005, IWLCS.

[3]  M. Friedman.  A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[4]  Geoffrey I. Webb,et al.  Discretization for naive-Bayes learning: managing discretization bias and variance , 2008, Machine Learning.

[5]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[6]  Zhang Lei,et al.  Designing of classifiers based on immune principles and fuzzy rules , 2008, Inf. Sci..

[7]  D. Quade.  Using Weighted Rankings in the Analysis of Complete Blocks with Additive Block Effects , 1979 .

[8]  Yixin Chen,et al.  Support vector learning for fuzzy rule-based classification systems , 2003, IEEE Trans. Fuzzy Syst..

[9]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[10]  Jianjun Li.  A two-step rejection procedure for testing multiple hypotheses , 2008 .

[11]  Robert G. D. Steel,et al.  A Multiple Comparison Sign Test: Treatments Versus Control , 1959 .

[12]  S. P. Wright,et al.  Adjusted P-values for simultaneous inference , 1992 .

[13]  Martin V. Butz,et al.  Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule , 2005, IWLCS.

[14]  J. L. Hodges,et al.  Rank Methods for Combination of Independent Experiments in Analysis of Variance , 1962 .

[15]  Pedro Antonio Gutiérrez,et al.  Evolutionary product-unit neural networks classifiers , 2008, Neurocomputing.

[16]  H. Finner.  On a Monotonicity Problem in Step-Down Multiple Test Procedures , 1993 .

[17]  Juan Julián Merelo Guervós,et al.  Evolving RBF neural networks for time-series forecasting with EvRBF , 2004, Inf. Sci..

[18]  Hisao Ishibuchi,et al.  Hybridization of fuzzy GBML approaches for pattern classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  D. Rom.  A sequentially rejective test procedure based on a modified Bonferroni inequality , 1990 .

[20]  B. Holland,et al.  An Improved Sequentially Rejective Bonferroni Test Procedure , 1987 .

[21]  K. Doksum.  Robust Procedures for Some Linear Models with one Observation per Cell , 1967 .

[22]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[23]  Grigorios Tsoumakas,et al.  Greedy regression ensemble selection: Theory and an application to water quality prediction , 2008, Inf. Sci..

[24]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[25]  Francisco Herrera,et al.  A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..

[26]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[27]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[28]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[29]  S. S. Young,et al.  Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment , 1993 .

[30]  O. J. Dunn.  Multiple Comparisons among Means , 1961 .

[31]  R. G. D. Steel,et al.  Tables for a Treatments Versus Control Multiple Comparisons Sign Test , 1965 .

[32]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[33]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[34]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[35]  Wei-Pang Yang,et al.  A discretization algorithm based on Class-Attribute Contingency Coefficient , 2008, Inf. Sci..

[36]  Y. Hochberg.  A sharper Bonferroni procedure for multiple tests of significance , 1988 .

[37]  Shusaku Tsumoto,et al.  Contingency matrix theory: Statistical dependence in a contingency table , 2009, Inf. Sci..

[38]  W. W. Daniel.  Applied Nonparametric Statistics , 1979 .

[39]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[40]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[41]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[42]  S. García,et al.  An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .

[43]  Seppo J. Ovaska,et al.  A general framework for statistical performance comparison of evolutionary computation algorithms , 2006, Inf. Sci..

[44]  R. Iman,et al.  Approximations of the critical region of the fbietkan statistic , 1980 .

[45]  G. Hommel.  A stagewise rejective multiple test procedure based on a modified Bonferroni test , 1988 .

[46]  Deborah R. Carvalho,et al.  A hybrid decision tree/genetic algorithm method for data mining , 2004, Inf. Sci..

[47]  D. Wolpert.  The Supervised Learning No-Free-Lunch Theorems , 2002 .