Journal Contributions: Accepted

Jump to Year:   2008

2008
	J.R. Cano, F. Herrera, M. Lozano. On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining. Applied Soft Computing (2008) In press			bib

 Journal Contributions: Published

Jump to Year:   2007  2006  2005  2004  2003  2002  2001  2000  1997

2007
	J.R. Cano, F. Herrera, M. Lozano. Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. Data and Knowledge Engineering 60:1 (2007) 90-108			bib
2006
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	R. Baumqartner, R.L. Somorjai. Data complexity assessment in undersampled classification of high-dimensional biomedical data. Pattern Recognition Letters 27:12 (2006) 1383-1389			bib
	J.W.F. Catto, M.F. Abbod, D.A. Linkens, F.C. Hamdy. Neuro-fuzzy modeling: An accurate and interpretable method for predicting bladder cancer progression. Journal of Urology 175:2 (2006) 474-479		bib
	A. Chandramohan, M.V.C. Rao. A novel approach for combining fuzzy rules using mean operators for effective rule reduction. Soft Computing 10:11 (2006) 1103-1108			bib
	D. Elizondo. The linear separability problem: Some testing methods. IEEE Transactions on Neural Networks 17:2 (2006) 330-344			bib
	L.A. Kurgan, K.J. Cios, S. Dick. Highly scalable and robust rule learner: Performance evaluation and comparison. IEEE Transactions on Systems, MAn, and Cybernetics, Part B: Cybernetics 36:1 (2006) 32-53			bib
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2005
	I. De Falco, A. Della Cioppa, A. Iazzetta, E. Tarantino. An evolutionary approach for automatically extracting intelligible classification rules. Knowledge and Information Systems 7:2 (2005) 179-201			bib
	M.S. Kim, C.H. Kim, J.J. Lee. Evolving structure and parameters of fuzzy models with interpretable membership functions. Journal of Intelligent and Fuzzy Systems 16:2 (2005) 95-105		bib
	Y. Li, M. Dong, R. Kothari. Classifiability-based omnivariate decision trees. IEEE Transactions on Neural Networks 16:6 (2005) 1547-1560			bib
	S. Papadimitriou, K. Terzidis. Mining interpretable fuzzy rules with support vector learning and outer-product fuzzy rule selection. WSEAS Transactions on Information Science and Applications 2:4 (2005) 380-389		bib
	A.C. Tan, D.Q. Naiman, L. Xu, R.L. Winslow, D. Geman. Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21:20 (2005) 3896-3904		bib
	H. Wang, S. Kwong, Y.C. Jin, W. Wei, K.F. Man. Agent-based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 35:2 (2005) 143-155			bib
	H. Wang, S. Kwong, Y.C. Jin, W. Wei, K.F. Man. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Sets and Systems 149:1 (2005) 209-233			bib
	Z.Y. Xing, L.M. Jia, Y. Zhang, W.L. Hu, Y. Qin. Case study of data-driven interpretable fuzzy modeling. Zidonghua Xuebao/Acta Automatica Sinica 31:6 (2005) 815-824		bib
2004
	E. Baralis, S. Chiusano. Essential classification rule sets. ACM Transactions on Database Systems 29:4 (2004) 635-674			bib
	M. Last, O. Maimon. A compact and accurate model for classification. IEEE Transactions on Knowledge and Data Engineering 16:2 (2004) 203-215			bib
	K. Muata, O. Bryson. Evaluation of decision trees: a multicriteria approach. Computers and Operations Research 31 (2004) 1933-1945			bib
2003
	M. Dong, R. Kothari. Feature subset selection using a new definition of classifiability. Pattern Recognition Letters 24:9-10 (2003) 1215-1225			bib
	R. Goodacre. Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules. Vibrational Spectroscopy 32:1 (2003) 33-45			bib
	L.O. Hall, K.W. Bowyer, R.E. Banfield, S. Eschrich, R. Collins. Is Error-based pruning redeemable?. International Journal on Artificial Intelligence Tools 12:3 (2003) 249-264			bib
	Y.C. Jin, B. Sendhoff. Extracting interpretable fuzzy rules from RBF networks. Neural Processing Letters 17:2 (2003) 149-164			bib
	S. Singh. Multiresolution Estimates of Classification Complexity. IEEE Transactions on Pattern Analysis and Machine Intelligence 25:12 (2003) 1534-1539			bib
	T. Sudkamp, A. Knapp, J. Knapp. Model generation by domain refinement and rule reduction. EEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 33:1 (2003) 45-55			bib
	C. Zhou, W. Xiao, T.M. Tirpak, P.C. Nelson. Evolving Accurate and Compact Classification Rules With Gene Expression Programming. IEEE Transactions on Evolutionary Computation 7:6 (2003) 519-531			bib
	Z.H. Zhou, Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine 7:1 (2003) 37-42			bib
2002
	M. Bramer. Using J-pruning to reduce overffiting in classification trees. Knowledge-Based Systems 15 (2002) 301-308			bib
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	J. Li, H. Shen, R. Topor. Mining the optimal class association rule set. Knowledge-Based System 15:7 (2002) 399-405			bib
	J. Zhang, S. Koper, A. Knoll. Extracting compact fuzzy rules based on adaptive data approximation using B-splines. Information Sciences 142:1-4 (2002) 227-248			bib
	M. Zorman, H.P. Eich, B. Stiglic, C. Ohmann, M. Lenic. Does size really matter-using a decision tree approach for comparison of three different databases from the medical field of acute appendicitis. Journal of Medical Systems 26:5 (2002) 465-477			bib
2001
	T. Elomaa, M. Kaariainen. An analysis of reduced error pruning. Journal of Artificial Intelligence Research 15 (2001) 163-187			bib
	S. Guillaume. Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems 9:3 (2001) 426-443			bib
2000
	M. Sebban, R. Nock, J.H. Chauchat, R. Rakotomalala. Impact of learning set quality and size on decision tree performances. International Journal of Computers, Systems and Signals 1:1 (2000) 85-105			bib
1997
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 Contributions to Books Chapters

Jump to Year:   2005  2004

2005
	J.R. Cano, F. Herrera, M. Lozano. A study on the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. In: F. Hoffmann, R. Roy, M. Koppen, F. Klawonn (Eds.) SOFT COMPUTING: METHODOLOGIES AND APPLICATIONS, SPRINGER-VERLAG BERLIN, 2005, 271-284			bib
2004
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 Conference Contributions

Jump to Year:   2006  2005  2004  2003  2002  2001  1999  1998  1997

2006
	F.J. Berlanga, M.J. del Jesus, M.J. Gacto, F. Herrera. A genetic-programming-based approach for the learning of compact fuzzy rule-based classification systems. International conference on. artificial intelligence and soft computing (ICAISC06). Lecture Notes in Computer Science 4029, Springer-Verlag 2006, Zakopane (Poland, 2006) 182-191			bib
	A. Chan, A.A. Freitas. A new classification-rule pruning procedure for an Ant Colony Algorithm. Evolution Artificielle (EA05). Lecture Notes in Computer Science 2871, Springer-Verlag 2006, Lille (France, 2006) 25-36			bib
	M.A. Esseqhir, G. Gasmi, S.B. Yahia, Y. Slimani. EGEA: A new hybrid approach towards extracting reduced generic association rule set (application to AML blood cancer therapy). 18th International Conference on Database and Expert Systems Applications (DEXA 2007). Lecture Notes in Computer Science 4081, 2006 (2006) 491-502			bib
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	E. Pranckeviciene, T.K. Ho, R. Somorjai. Class separability in spaces reduced by feature selection. Proceedings - International Conference on Pattern Recognition 3 (ICPR 2006). (2006) 254-257			bib
	F. Vasile, A. Silvescu, D.-K. Kang, V. Honavar. TRIPPER: Rule learning using taxonomies. Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). Lecture Notes in Computer Science 3918, 2006 (2006) 55-59			bib
2005
	E. Hernandez, J.A. Carrasco, J.F. Martinez. Classifier Selection Based on Data Complexity Measures. X Congreso Iberoamericano de Reconocimiento de Patrones, CIARP 2005 (CIARP 2005). Lecture Notes in Computer Science 3773, 2005 (2005) 586-592			bib
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2004
	J.R. Cano, F. Herrera, M. Lozano. Evolutionary stratified instance selection applied to training set selection for extracting high precise-interpretable classification rules. International Conference on Data Mining ((ICDM'2004)). Brightom (England, 2004) 0-0			bib
	J.R. Cano, F. Herrera, M. Lozano. Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules. IEEE ICDM 2004 Workshop on Alternative Techniques for Data Mining and Knwoledge Discovery. Brightom (England, 2004) 0-0		bib
	R.P.W. Duin, E. Pekalska, D.M.J. Tax. The characterization of classification problems by classifier disagreements. Proceedings - International Conference on Pattern Recognition 1 (ICPR 2004). (2004) 140-143			bib
	E.B. Mansilla, T.K. Ho. On classifier domains of competence . Proceedings - International Conference on Pattern Recognition 1 (ICPR 2004). (2004) 136-139			bib
	A. Riid, E. Rustern. Heuro-fuzzy extraction of interpretable fuzzy rules from data. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 3. (2004) 2266-2271			bib
	K. Yamamoto, T. Furuhashi, T. Yoshikawa. A proposal of visualization method for obtaining interpretable fuzzy rules. IEEE International Conference on Fuzzy Systems 2. (2004) 1013-1018			bib
2003
	L.O. Hall, R. Collins, K.W. Bowyer, R.E. Banfield. Error-based pruning of decision trees grown on very large data sets can work!. IEEE International Conference on Tools for Artificial Intelligence (ICTAI'2002). Washington DC (United States of America, 2003) 233-238			bib
2002
	O. Kaynak, K. Jezernik, A. Szeghegvi. Complexity reduction of rule based models: A survey. IEEE International Conference on Plasma Science 2. (2002) 1216-1221			bib
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2001
	J. Li, H. Shen, R. Topor. Mining the smallest association rule set for prediction. IEEE International Conference on Data Mining (ICDM'01). San Jose (USA, 2001) 361-368			bib
1999
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1998
	T. Oates, D. Jensen. Large datasets lead to overly complex models: an explanation and a solution. IV International Conference on Knowledge Discovery and Data Mining (KDD-98). New York (USA, 1998) 294-298			bib
1997
	T. Oates, D. Jensen. The effects of training set size on decision tree complexity. Fourteenth International Conference on Machine Learning (ICML'97). Tenessee (USA, 1997) 254-262			bib

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