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Additional Bibliography
Data Complexity: An Overview and New Challenges.
T.K. Ho, Bell Laboratories, USA
- E. Bernadó-Mansilla, T.K. Ho. Domain of Competence of XCS Classifier System in Complexity Measurement Space. IEEE Transactions on Evolutionary Computation 9:1 (2005) 82-104
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T.K. Ho. A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors. Pattern Analysis and Applications 5 (2002) 102-112
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T.K. Ho, M. Basu. Complexity Measures of Supervised Classification Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:3 (2002) 289-300
On the necessity of dataset characterization for experimental analysis.
Towards artificial datasets.
Nuria Macia, University Ramón Llull, Barcelona
- Demsar, J., Statistical comparisons of classifiers over multiple data sets. Journal of
Machine Learning Research. Vol. 7. pp. 1–30. 2006.
Some studies on the construction of artificial data sets for some data
complexity measures. Evolutionary prototype selection evaluated with an overlapping measure.
José Ramón Cano, University of Jaén, Jaén
- E. Bernadó-Mansilla, T.K. Ho. Domain of Competence of XCS Classifier System in Complexity Measurement Space. IEEE Transactions on Evolutionary Computation 9:1 (2005) 82-104
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T.K. Ho, M. Basu. Complexity Measures of Supervised Classification Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:3 (2002) 289-300
- J.M. Sotoca, R.A. Mollineda, J.S. Sánchez. A meta-learning
framework for pattern classification. Revista Iberoamericana de Inteligencia Artificial
29 (2006) 31-38
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R.A. Mollineda, J.S. Sánchez, J.M. Sotoca. Data Characterization for Effective Prototype
Selection. First edition of the Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2005).
Lecture Notes in Computer Science 3523, Springer-Verlag 2005 (2005) 27-34
Introduction to Imbalanced data sets. Some results on the use of evolutionary
prototype selection for imbalanced data sets.
Salvador García, University of Granada, Granada
- S. García and F. Herrera, Evolutionary Under-Sampling for Classification with
Imbalanced Data Sets: Proposals and Taxonomy. Evolutionary Computation, in press (2008).
Some results on the use of UCS for imbalanced data sets.
Albert Orriols, University Ramón Llull, Barcelona
- A. Orriols-Puig, E. Bernadó-Mansilla. Mining Imbalanced Data with Learning Classifier Systems. In:
L. Bull, E. Bernadó-Mansilla, J. Holmes (Eds.) Learning Classifier Systems in Data Mining,
Springer, in press (2008).
- A. Orriols-Puig, E. Bernadó-Mansilla. Evolutionary Rule-Based Systems for
Imbalanced Datasets. Soft Computing Journal. Special Issue on Evolutionary and
Metaheuristic-based Data Mining (EMBDM), in press (2008) .
- A. Orriols-Puig, D.E. Goldberg, K. Sastry, E. Bernadó-Mansilla.
Modeling XCS in Class Imbalances: Population Size and Parameter Settings.
2007 Genetic and Evolutionary Computation Conference (GECCO'07). London (UK, 2007) 1838-1845
Some results on the use of Fuzzy Rule Based Systems for imbalanced data sets.
Alberto Fernández, University of Granada, Granada
- A. Fernandez, S. García, M.J. del Jesus, F. Herrera,
A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems
in the Framework of Imbalanced Data Sets. Fuzzy Sets and Systems, doi: 10.1016/j.fss.2007.12.023,
in press (2008).
Memetic Pittsburgh LCS.
Jaume Barcardit, University of Nottingham, United Kingdom
- J. Bacardit and N. Krasnogor.
Smart Crossover operator with multiple parents for a Pittsburgh Learning Classifier System
In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 1441 - 1448, ACM Press, 2006
HIDER Method and natural codification.
Raul Giráldez, University Pablo Olavide, Sevilla
- Jesus Aguilar–Ruiz, Jaume Bacardit, and Federico Divina. Experimental Evaluation of Discretization
Schemes for Rule Induction. Proceedings of the GECCO 2004, LNCS 3102, pp. 828–839, 2004
- Jesús S. Aguilar-Ruiz, Raul Giraldez, José C. Riquelme. Natural Encoding for Evolutionary Supervised Learning
IEEE Transactions on Evolutionary Computation 11:4, 466-479 (2007)
- Jesús S. Aguilar-Ruiz, José C. Riquelme, and Miguel Toro. Evolutionary Learning of Hierarchical Decision Rules
IEEE Transactions on Systems, Man, and Cybernetics—part B: Cybernetics 33:2, 324-331 (2003)
- R. Giráldez Improving the performance of evolutionary algorithms for decision rule
learning. AI Communications 18, 63–65 (2005)
- Jesús S. Aguilar-Ruiz and Raul Giraldez. Feature Influence for Evolutionary Learning.
Proceedings of the GECCO’05 1139-1145 (2005)
- R. Giráldez, J.S. Aguilar–Ruiz, J.C. Riquelme and F.J. Ferrer–Troyano.
Discretization oriented to Decision Rules Generation. IOS Press, pp.275-279 (KES)
- Raúl Giráldez, Jesús S. Aguilar-Ruiz, and José C. Riquelme.
IEEE Transactions on Systems, Man, and Cybernetics—part C: Applications and Reviews 35:2, 254-261 (2005)
A study of statistical techniques and performance of GBML.
Julián Luengo, University of Granada, Granada
- S. García, A. Fernandez, A.D. Benítez, F. Herrera, Statistical Comparisons
by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning.
Proceedings of the II Congreso Español de Informática (CEDI 2007). V Taller Nacional de
Minería de Datos y Aprendizaje (TAMIDA 2007), Zaragoza (Spain), 95-104, 11-14 September 2007.
- S. García, D. Molina, M. Lozano, F. Herrera, 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. Journal of Heuristics,
doi: 10.1007/s10732-008-9080-4, in press (2008)
- Demsar, J., Statistical comparisons of classifiers over multiple data sets. Journal of
Machine Learning Research. Vol. 7. pp. 1–30. 2006.
Incorporating fuzzy rules in LCS: Fuzzy XCS and Fuzzy UCS.
Jorge Casillas, University of Granada, Granada
- J. Casillas, B. Carse, L. Bull, Fuzzy-XCS: a Michigan genetic fuzzy system, IEEE Transactions on Fuzzy Systems
15:4 (2007) 536-550. doi:10.1109/TFUZZ.2007.900904.
- A. Orriols-Puig, J. Casillas, E. Bernadó-Mansilla, Fuzzy-UCS:
a Michigan-style learning fuzzy-classifier system for supervised learning,
IEEE Transactions on Evolutionary Computation, in press (2008).
LCS: New trends.
Albert Orriols, University Ramón Llull, Barcelona
GBML methods for large-scale datasets.
Jaume Bacardit, University of Nottingham, United Kingdom
- J. Bacardit and N. Krasnogor. Fast Rule Representation for Continuous Attributes in Genetics-Based Machine Learning.
In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
(GECCO2008), to appear, ACM Press, 2007
- M. Stout, J. Bacardit, J.D. Hirst and N. Krasnogor.
Prediction of Recursive Convex Hull Class Assignments for Protein Residues.
Bioinformatics, 24(7):916-923, 2008
- J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llorà and N. Krasnogor.
Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction.
In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007), pp. 346-353, ACM Press, 2007
- J. Bacardit and N. Krasnogor.
Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System.
Ninth International Workshop on Learning Classifier Systems, IWLCS2006
- J. Bacardit, M. Stout, J.D. Hirst, N. Krasnogor and J. Blazewicz.
Coordination number prediction using Learning Classifier Systems: Performance and interpretability.
In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 247-254, ACM Press, 2006
GP-COACH: Genetic Programming based learning of COmpact and ACcurate fuzzy rule based classification
systems for High dimensional problems.
Francisco Berlanga, University of Jaén, JAEN
- F.J. Berlanga, M.J. del Jesus, F. Herrera.
A Novel Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method
using Genetic Programming for High Dimensional Problems.
3rd International Workshop on Genetic and Evolving Fuzzy
Systems (GEFS08). WittenBommerholz (Germany, 2008) 101-106
Introduction to Multi-instance learning: Foundations and applications.
Some results on the use of Genetic Programming for MIL.
Amelia Zafra, University of Córdoba, Córdoba
- A. Zafra, S. Ventura, E. Herrera-Viedma, and C. Romero.
Multiple Instance Learning with Genetic Programming for Web Mining.
In F. Sandoval et al. (Eds.): IWANN 2007, Lecture Notes in Computer Science 4507,
pp. 919–927, 2007.
- A. Zafra and S. Ventura. Multi-objective Genetic Programming for Multiple Instance Learning. In J.N. Kok et al. (Eds.)
ECML 2007, Lecture Notes in Artificial Intelligence 4701, pp. 790–797, 2007.
Introduction to EDAs. Learning linguistic fuzzy rules with EDAs: an application to breeding
value prediction in Manchego sheep.
José Antonio Gámez, University of Castilla la Mancha, Albacete
- L. de la Ossa, J. Flores, J.A. Gámez, J.L. Mateo, and J.M. Puerta.
Initial breeding value prediction on manchego sheep by using rule-based systems.
Expert Systems with Applications, 33(1):96-109, 2007
- J. Flores, J.A. Gámez and J.M. Puerta. Learning linguistic fuzzy
rules by using estimation of distribution algorithm as the search engine
in the COR methodology. In: Towards a new evolutionary computation.
Advances in Estimation of Distribution Algorithms.
Volume 192 of Studies in Fuzziness and Soft Computing., Springer Velarg (2005) 259-280
- J.A. Gámez L. de la Ossa and J.M. Puerta.
Learning weighted linguistic fuzzy rules with estimation of distribution algorithms.
In Proceedings of IEEE Congress on Evolutionary Computation , CEC2006, pages 3242-3249, 2006
Introduction to Subgroup Discovery. Some results on the evolutionary extraction
of fuzzy rules for subgroup discovery.
Pedro González, University of Jaén, Jaén
- M.J. del Jesus, P. González, F. Herrera, M. Mesonero. Evolutionary fuzzy
rule induction process for subgroup discovery: a case study in marketing.
IEEE Transactions on Fuzzy Systems 15:4 (2007) 578-592
- C. Romero, P. González, S. Ventura, M.J. del Jesus, F. Herrera. Evolutionary
algorithms for subgroup discovery in e-learning: A practical application
using Moodle data. Expert Systems with Applications, in press (2008)
- P. González, M.J. del Jesus, F. Herrera. Multiobjective genetic algorithm
for extractiong subgroup discovery fuzzy rules. 2007 IEEE Symposium on
Computational Intelligence in Multicriteria Decision Making (IEEE MCDM
2007). Omnipress. Honolulu (USA, 2007) 50-57
Introduction to Low quality data.
Some results on the use of Evolutionary Algorithms for extraction knowledge from low quality data.
Luciano Sánchez, University of Oviedo, Gijón
- L. Sánchez, J. Otero, J.R. Villar.
Boosting of fuzzy models for high-dimensional imprecise datasets.
11th International Conference on Information Processing and Management (IPMU2006). Paris (France, 2006) 1965-1973
- L. Sánchez, I. Couso, J. Casillas. Modeling vague data with
genetic fuzzy systems under a combination of crisp and imprecise
criteria. First IEEE Symposium on Computational Intelligence in Multi-Criteria
Decision-Making (MCDM 2007). Honolulu (USA, 2007) 30-37
- A. Otero, J. Otero, L. Sánchez, J.R. Villar. Longest path estimation
from inherently fuzzy data acquired with GPS using genetic algorithms.
2nd International Symposium on Evolving Fuzzy Systems 2006 (EFS06).
Ambleside Lake District (UK, 2006) 300-306
- L. Sánchez, I. Couso. Advocating the use of Imprecisely Observed Data in Genetic Fuzzy Systems.
IEEE Transactions on Fuzzy Systems 15:4 (2007) 551-562
- I. Couso, D. Dubois, S. Montes, L. Sánchez. On various definitions of the variance of a fuzzy random variable. 5th International Symposium on
Imprecise Probabilities and Their Applications (ISIPTA07). Prague (Czech Republic, 2007) 135-144
- L. Sánchez, J. Otero. Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms. Proceedings of the 16th IEEE
International Conference on Fuzzy Systems (FUZZ-IEEE07). London (United Kingdom, 2007) 1921-1926
- L. Sánchez, M.R. Suárez, J.R. Villar, I. Couso. Some Results about Mutual
Information-based Feature Selection and Fuzzy Discretization of Vague Data.
Proceedings of the 16th IEEE International Conference on Fuzzy Systems
(FUZZ-IEEE07). London (United Kingdom, 2007) 1958-1963
- I. Couso, L. Sánchez. Higher order models for fuzzy random variables. Fuzzy Sets and Systems 159:3 (2008) 237-258
- L. Sánchez, A. Palacios, M. R. Suárez and I. Couso. Graphical exploratory analysis of vague data in the
early diagnosis of dyslexia. Proceedings of the IPMU'08, in press (2008).
- I. Couso and L. Sánchez. Defuzzification of fuzzy p-values. Proceedings of the
Fouth International Workshop on Soft Methods, Probability and Statistics (SMPS08), Toulouse (France), in press 2008.
Introduction to Multi-objective Learning.
Some resuls on the use of MOEAs for tuning FRBSs parameters.
Rafael Alcalá, University of Granada, Granada
- M.J. Gacto, R. Alcalá, F. Herrera, Adaptation and Application of Multi-Objective Evolutionary Algorithms
for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems. Soft Computing, in press (2008)
- M.J. Gacto, R. Alcalá, F. Herrera, Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design
Process. Proceedings of the 12th International Conference on Information Processing and
Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), in press (2008)
Towards evolving consistent, complete and compact fuzzy rule sets: A genetic multiobjective approach.
Jorge Casillas, University of Granada, Granada
- J. Casillas, P. Martínez, A.D. Benítez, Learning consistent, complete and compact sets of fuzzy
rules in conjunctive normal form for regression problems, Soft Computing (2008). In press.
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