Journal Contributions: Published

Jump to Year:
        2009  2007  2006  2005  2004  2003  2002  2001  2000  1999
        1998  1997  1996  1995  1994  1991  1989  1988  1987  1986
        1979  1975

2009
	P.P. Balestrassi, E. Popova, A.P. Paiva, J.W. Marangon-Lima. Design of experiments on neural network's training for nonlinear time series forecasting. Neurocomputing 72:4-6 (2009) 1160-1178			bib
2007
	A. Adler, M.E. Schuckers. Comparing Human and Automatic Face Recognition Performance. IEEE Transactions on Systems, Man, and Cybernetics, Part B 37:5 (2007) 1248-1255			bib
	R.E. Banfield, L.O. Hall, K.W. Bowyer, W.P. Kegelmeyer. A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence 29:1 (2007) 173-180			bib
	S.Y. Sohn, H.W. Shin. Experimental study for the comparison of classifier combination methods. Pattern Recognition 40:1 (2007) 33-40			bib
	Y. Yang, G.I. Webb, J. Cerquides, K.B. Korb, J. Boughton, K.M. Ting. To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Transactions on Knowledge and Data Engineering 19:12 (2007) 1652-1665			bib
2006
	A. Ali, K.A. Smith. On learning algorithm selection for classification. Applied Soft Computing 6:2 (2006) 119-138			bib
	J. Demsar. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7 (2006) 1-30			bib
	C. Drummond, R.C. Holte. Cost Curves: An improved method for visualizing classifier performance. Machine Learning 65:1 (2006) 95-130			bib
	C.-X. J Feng, Z.-G. Yu, A. Kusiak. Selection and validation of predictive regression and neural network models based on designed experiments. IIE TRANSACTIONS 38 (2006) 13-23			bib
	E. Lamma, P. Mello, A. Nanetti, F. Riguzzi. Artificial intelligence techniques for monitoring dangerous infections. IEEE Transactions on Information Technology in Biomedicine 10:1 (2006) 143-155			bib
	B. Mascialino, A. Pfeiffer, M.G. Pia, A. Ribon, P. Viarengo. New Developments of the Goodness-of-Fit Statistical Toolkit. IEEE Transactions on Nuclear Science 53:6 (2006) 3834-3841			bib
	O.T. Yildiz, E. Alpaydin. Ordering and finding the best of K > 2 supervised learning algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 28:3 (2006) 392-402			bib
	O.T. Yildiz, E. Alpaydin. Ordering and finding the best of K > 2 supervised learning algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 28:3 (2006) 392-402			bib
2005
	W.T. Barry, A.B. Nobel, F.A. Wright. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21:9 (2005) 1943-1949			bib
	U.M. Braga-Neto, E.R. Dougherty. Exact performance of error estimators for discrete classifiers. Pattern Recognition 38:11 (2005) 1799-1814			bib
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	W.J. Fu, R.J. Carroll, S. Wang. Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics 21:9 (2005) 1979-1986			bib
	M. Gal-Or, J.H. May, W.E. Spangler. Assessing the predictive accuracy of diversity measures with domain-dependent, asymmetric misclassication costs. Information Fusion 6:1 (2005) 37-48			bib
	T. Hothorn, F. Leisch, A. Zeileis, K. Hornik. The Design and Analysis of Benchmark Experiments. Journal of Computational & Graphical Statistics 14:3 (2005) 675-699			bib
	J.W. Lee, J.B. Lee, M. Park, S.H. Song. An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis 48:4 (2005) 869-885			bib
	D.Y. Lin. An efficient Monte Carlo approach to assessing statistical significance in genomic studies. Bioinformatics 21:6 (2005) 781-787			bib
	M. Markatou, H. Tian, S. Biswas, G. Hripcsak. Analysis of Variance of Cross-Validation Estimators of the Generalization Error. Journal of Machine Learning Research 0:6 (2005) 1127-1168			bib
	A.M. Molinaro, R. Simon, R.M. Pfeiffer. Prediction error estimation: a comparison of resampling methods. Bioinformatics 21:15 (2005) 3301-3307			bib
	L. Rueda. A one-dimensional analysis for the probability of error of linear classifiers for normally distributed classes. Pattern Recognitio 38:8 (2005) 1197-1207			bib
	C. Sima, S.N. Attoor, U.M. Braga-Neto, J. Lowey, E. Suh. Impact of error estimation on feature selection. Pattern Recognition 0:38 (2005) 2472-2482			bib
	Y. Pawitan, S. Michiels, S. Koscielny, A. Gusnanto, A. Ploner. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 21:13 (2005) 3017-3024			bib
2004
	Y. Bengio, Y. Granvalet.. No Unbiased Estimator of the Variance of K-Fold Cross-Validation. Journal of Machine Learning Research 5 (2004) 1089-1105			bib
	U.M. Braga-Neto, E.R. Dougherty. Bolstered error estimation. Pattern Recognition 0:37 (2004) 1267-1281			bib
	U.M. Braga-Neto, E.R. Dougherty. Is cross-validation valid for small-sample microarray classification?. Bioinformatics 20:3 (2004) 374-380			bib
	U.M. Braga-Neto, R. Hashimoto, E.R. Dougherty, D.V. Nguyen, R.J. Carroll. Is cross-validation better than resubstitution for ranking genes?. Bioinformatics 20:2 (2004) 253-258			bib
	S. Dzeroski, B. Zenko. Is combining classifiers with stacking better than selecting the best one?. Machine learning 0:54 (2004) 255-273			bib
	R.L. Fernando, D. Nettleton, B.R. Southey, J.C.M. Dekkers, M.F. Rothschild, M. Soller. Controlling the proportion of false positives in multiple dependent tests. Genetics 0:166 (2004) 611-619			bib
	Y. Granvalet.. Bagging Equalizes Influence. Machine learning 0:55 (2004) 251-270			bib
	R. Mansourian, D.M. Mutch, N. Antille, J. Aubert, P. Fogel, J. Le Goff, J. Moulin, A. Petrov, A. Rytz, J.J. Voegel, M.A. Roberts. The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data. Bioinformatics 20:16 (2004) 2726-2737			bib
	R. Rifkin, A. Klautau. In Defense of One-Vs-All Classification. Journal of Machine Learning Research 0:5 (2004) 101-141			bib
	P. Van der Putten, M. Van Someren. Benchmarking Least Squares Support Vector Machine Classifiers. Machine Learning 0:54 (2004) 5-32			bib
2003
	S.V. Beiden, M.A. Maloof, R.F. Wagner. A General Model for Finite-Sample Effects in Training and Testing of Competing Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 25:12 (2003) 1561-1569			bib
	P.B. Brazdil, C. Soares, J.P. Da Costa. Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 0:50 (2003) 251-277			bib
	G.C. Cawley, N.L.C. Talbot.. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition 36:11 (2003) 2585-2592			bib
	S.W. Cole, Z. Galic, J.A. Zack. Controlling false-negative errors in microarray differential expression analysis: a PRIM approach. Bioinformatics 19:14 (2003) 1808-1816			bib
	C. Nadeau, Y. Bengio. Inference for the generalization error. Machine learning 0:52 (2003) 239-281			bib
	E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, V.G. Da Fonseca. Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7:2 (2003) 117-132			bib
2002
	D.G. Bonett, E. Seier. A test of normality with high uniform power. Computational Statistics & Data Analysis 40 (2002) 435-445			bib
	N. Duffy, D. Helmbold. Boosting methods for regression. Machine learning 0:47 (2002) 153-200			bib
	J. Pizarro, E. Guerrero, P. Galindo. Multiple comparison procedures applied to model selection. Neurocomputing 48 (2002) 155-173			bib
	J.A. Rafter, M.L. Abell, J.P. Braselton. Multiple Comparison Methods for Means. Society for Industrial and Applied Mathematic 44:2 (2002) 259-278			bib
	J.D. Storey. A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B 0:64 (2002) 479-498			bib
2001
	J.F. Bradford, C.E. Brodley. The Effect of Instance-Space Partition on Significance. Machine Learning 0:42 (2001) 269-286			bib
	C.C. McGeoch. Experimental Analysis of Algorithms. Notices of the American MAthematical Society 48:3 (2001) 304-311			bib
2000
	D. Curran-Everett. Multiple comparisons: philosophies and illustrations. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology 279:1 (2000) 0-0			bib
	N.A. Diamantidis, D. Karlis, E.A. Giakoumakis. Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intelligence 0:116 (2000) 1-16			bib
	A.K. Jain, R.P.W. Duin, J. Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22:1 (2000) 4-37			bib
	T.S. Lim, W.Y. Loh. A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 0:40 (2000) 203-228			bib
	G.I. Webb. MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 0:40 (2000) 159-196			bib
1999
	E. Alpaydin. Combined 5x2 cv F Test for Comparing Supervised Classification Learning Algorithms. Neural Computation 11:8 (1999) 1885-1892			bib
	E. Bauer, R. Kohavi. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 1-2:36 (1999) 105-142			bib
	I. Rivals, L. Personnaz. On Cross-Validation for Model Selection. Neural Computation 11:4 (1999) 863-870			bib
	S.L. Salzberg. On Comparing Classifiers: A Critique of Current Research and Methods. Data Mining and Knowledge Discovery 0:1 (1999) 1-12			bib
1998
	M. Adya, F. Collopy. How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation. Journal of Forecasting 0:17 (1998) 481-495			bib
	T.G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10:7 (1998) 1895-1924			bib
	B. LeBaron, A.S. Weigend. A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series. IEEE Transactions on Neural Networks 9:1 (1998) 213-220			bib
1997
	S.L. Salzberg. On Comparing Classifiers: Pitfalls to Avoidanda Recommended Approach. Data Mining and Knowledge Discovery 0:1 (1997) 317-328			bib
1996
	L. Breiman. Heuristics of instability and stabilization in model selection. Annals of Statistics 24:6 (1996) 2350-2383			bib
	R.J. Cook, V.T. Farewell. Multiplicity Considerations in the Design and Analysis of Clinical Trials. Journal of the Royal Statistical Society: Series A 0:159 (1996) 93-110			bib
1995
	Y. Benjamini, T. Hochberg. Controlling the False Discovery Rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B 0:85 (1995) 289-300			bib
	J.P. Shaffer. Multiple Hypothesis Testing. Annual Review of Psychology 46 (1995) 561-584			bib
1994
	O. Etzioni, R. Etzioni. Statistical Method for Analyzing Speedup Learning Experiments. Machine Learning 0:14 (1994) 333-347			bib
1991
	S.J. Raudys, A.K. Jain. Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners. IEEE Trans. Pattern Analysis and Machine Intelligence 13:3 (1991) 252-264			bib
1989
	K. Fukunaga, R.R. Hayes. Estimation of Classifier Performance. IEEE Trans. Pattern Analysis and Machine Intelligence 11:10 (1989) 1087-1101			bib
	K. Fukunaga, R.R. Hayes. Effects of Sample Size in Classifier Design. IEEE Trans. Pattern Analysis and Machine Intelligence 11:8 (1989) 873-885			bib
1988
	Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75:4 (1988) 800-802			bib
	G. Hommel. A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 0:75 (1988) 383-386			bib
	P.C. O'Brien, M.A. Shampo. Statistical considerations for performing multiple tests in a single experiment. Mayo Clinic Proceedings 63 (1988) 813-815			bib
1987
	A.K. Jain, R.C. Dubes, C.C. Chen. Bootstrap techniques for error estimation. IEEE Transactions on Pattern Analysis and Machine Intellligence 9:5 (1987) 628-633			bib
1986
	R.J. Simes. An improved Bonferroni procedure for multiple tests of significance. Biometrika 0:73 (1986) 751-754			bib
1979
	S. Holm. A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics 0:6 (1979) 65-70			bib
1975
	I. Einot, K.R. Gabriel. A Study of the Powers of Several Methods of Multiple Comparison. Journal of the American Statistical Association 0:70 (1975) 574-583			bib

 Contributions to Books Chapters

Jump to Year:   2002

2002
	D.S. Johnson. A theoretician's guide to the experimental analysis of algorithms. In: D.S. Johnson (Eds.) Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, American Mathematical Society, 2002, 215-251			bib

 Conference Contributions

Jump to Year:   2008  2004  2003  2002  2001  2000  1998  1996  1995

2008
	D. Hu, X. Yu, Y. Feng. Randomization Test for Importance Degree of Variables in Rough Set Theory. Computational Intelligence and Industrial Application (PACIIA08). Wuhan (China, 2008) 107-111			bib
2004
	R.R. Bouckaert, E. Frank. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. VIII Pacific-Asia Conference of Advances in Knowledge Discovery and Data Mining (PAKDD'04). Lecture Notes in Computer Science 3056, Springer 2004, Sydney (Australia, 2004) 0-0			bib
	J. Menke, T.R. Martinez. Using permutations instead of student's t distribution for p-values in paired-difference algorithm comparisons. 2004 IEEE International Joint Conference on Neural Networks (IJCNN2004). Budapest (Hungary, 2004) 1331-1335			bib
2003
	R.R. Bouckaert. Choosing between two learning algorithms based on calibrated tests. Twentieth International Conference on Machine Learning (ICML 2003). Washington DC (USA, 2003) 94-99			bib
2002
	T. Beielstein, S. Markon. Threshold Selection, Hypothesis Tests, and DOE Methods. Evolutionary Computation 2002 (EC2002). Honolulu (USA, 2002) 777-782			bib
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	P. Popela, J. Roupec, P. Osmera, R. Matousek. The formal stochastic framework for comparison of genetic algorithms. Congress on Evolutionary Computation, 2002 (CEC'2002). Honolulu (EEUU, 2002) 576-581			bib
	P. Popela, J. Roupec, P. Osmera, R. Matousek. The formal stochastic framework for comparison of genetic algorithms. 2002 Congress on Evolutionary Computation, (CEC'2002). Honolulu (USA, 2002) 576-581			bib
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2001
	V. Grunert, C.M. Fonseca, M.A. Hall. Inferential performance assesment of stochastic optimisers and the attainment function. Evolutionary Multi-Criterion Optimization. First International Conference (EMO 2001). Lecture Notes in Computer Science 1993, Springer 2001, Zurich (Switzerland, 2001) 213-225			bib
	V. Grunert, C.M. Fonseca, A.O. Hall. Inferential performance assessment of stochastic optimisers and the attainment function. First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001). Lecture Notes in Computer Science 1993, Springer 2001, Zurich (Switzerland, 2001) 213-225			bib
	G. Shakhnarovich, R. El-Yaniv, Y. Baram. Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. International Conference on Machine Learning (ICML'01). Williamstown MA (USA, 2001) 521-528			bib
	G. Shakhnarovich, R. El-Yaniv, Y. Baram. Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation. International Conference on Machine Learning 2001 (ICML'01). Williamstown MA (USA, 2001) 521-528			bib
2000
	P.B. Brazdil, C. Soares. A Comparison of Ranking Methods for Classification Algorithm Selection. A Comparison of Ranking Methods for Classification Algorithm Selection (ECML'2000). Barcelona (Spain, 2000) 63-74			bib
	P. Domingos. A unified bias-variance decomposition for zero-one and squared loss. Seventeenth National Conference on Artificial Intelligence (AAAI-00). Austin TX (USA, 2000) 1-6			bib
	D.D. Margineantu, T.G. Dietterich. Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers. 17th International Conf. on Machine Learning (ICML2000). Morgan Kaufmann, San Francisco, CA. Stanford CA (USA, 2000) 583-590			bib
	M. Mullin, R. Sukthankar. Complete Cross-Validation for Nearest Neighbor Classifiers. International Conference on Machine Learning (ICML'00). Standford (USA, 2000) 0-0			bib
1998
	J.H. Piater, P.R. Cohen, X. Zhang, M. Atighetchi. A Randomized ANOVA Procedure for Comparing Performance Curves. Fifteenth International Conference on Machine Learning (ICML-98). Madison WI (USA, 1998) 430-438			bib
1996
	C.C. McGeoch. A bibliography of algorithm experimentation. 5th DIMACS Challenge Workshop (DIMACS96). Piscataway (USA, 1996) 0-0			bib
1995
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 Journal Contributions: Published

Jump to Year:   2009  2008  2004  2002  1999  1995  1987

2009
	G. Kou, Y. Peng, Z. Chen, Y. Shi. Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection. Information Sciences 179:4 (2009) 371-381			bib
2008
	Z. Bosnić, I. Kononenko. Comparison of approaches for estimating reliability of individual regression predictions. Data & Knowledge Engineering 67:3 (2008) 504-516			bib
	S. Lessmann, B. Baesens, C. Mues, S. Pietsch. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering 34:4 (2008) 485-496			bib
2004
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	A. Czarn, C. MacNish, K. Vijayan, B. Turlach, R. Gupta. Statistical Exploratory Analysis of Genetic Algorithms. IEEE Transactions on Evolutionary Computation 8:4 (2004) 405-421			bib
	K.F. Manly, D. Nettleton, J.T. Gene-Hwang. Genomics, Prior Probability, and Statistical Tests of Multiple Hypotheses. Genome Research 14 (2004) 997-1001			bib
2002
	D. Whitley, J.P. Watson, A. Howe, L. Barbulescu. Testing, Evaluation and Performance of Optimization and Learning Systems. Keynote Address: Adaptive Computing in Design and Manufacturing 0 (2002) 0-0			bib
1999
	C.H. Chen, S.D. Wu, L. Dai. Ordinal comparison of heuristic algorithms using stochastic optimization. IEEE Transactions on Robotics and Automation 15:1 (1999) 44-56			bib
1995
	R.S. Barr, B.L. Golden, J.P. Kelly, M.G.C. Resende, W.R. Stewart. Designing and Reporting on Computacional Experiments with Heuristics. Journal of Heuristic 1:1 (1995) 9-32		bib
	J.H. Hooker. Testing Heuristics: We Have it All Wrong. Journal of Heuristics 1:1 (1995) 33-42			bib
1987
	R.E. Nance, R.L. Moose, R.V. Foutz. A Statistical Technique for Comparing Heuristics: An Example from Capacity Assignment Strategies in Computer Network Design. Communication of the ACM 30:3 (1987) 430-442			bib

 Conference Contributions

Jump to Year:   2005  2003  1999

2005
	C.M. Fonseca, V. Grunert, L. Paquete. Exploring the performance of stochastic multiobjective optimisers with the second-order attainment function. Evolutionary Multi-Criterion Optimization (EMO05). Lecture Notes in Computer Science 3410, Springer-Verlag 2005 (2005) 250-264			bib
	J. Knowles. A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005). IEEE Computer Society. Wroclaw (pOLAND, 2005) 552-557			bib
2003
	T. Okabe, Y. Jin, B. Sendhoff. A critical survey of performance indices for multi-objective optimisation. The 2003 Congress on Evolutionary Computation, 2003. (CEC'2003). Canberra (Australia, 2003) 878-885			bib
1999
	K.J. Shaw, A.L., M. Thompson, J. Love, P.J. Fleming, C.M. Fonseca. Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem. 1999 Congress on Evolutionary Computation (CEC 99). IEEE. Washington (USA, 1999) 37-45			bib

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