Improving the scalability of rule-based evolutionary learning

Bacardit J (2004) Pittsburgh genetics-based machine learning in the data mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Spain

Bacardit J (2005) Analysis of the initialization stage of a pittsburgh approach learning classifier system. In: GECCO 2005: Proceedings of the genetic and evolutionary computation conference, ACM Press, vol 2, pp 1843–1850

Bacardit J, Butz MV (2007) Data mining in learning classifier systems: Comparing xcs with gassist. In: Advances at the frontier of Learning Classifier Systems, Springer-Verlag, pp 282–290. doi: 10.1007/978-3-540-71231-2_19

Bacardit J, Krasnogor N (2006a) Biohel: bioinformatics-oriented hierarchical evolutionary learning. Nottingham eprints, University of Nottingham

Bacardit J, Krasnogor N (2006b) Empirical evaluation of ensemble techniques for a pittsburgh learning classifier system. In: Ninth international workshop on learning classifier systems (IWLCS 2006), Springer, Lecture Notes in Artificial Intelligenge. http://www.asap.cs.nott.ac.uk/publications/pdf/iwlcs2006.pdf (to appear)

Bacardit J, Goldberg D, Butz M, Llorà X, Garrell JM (2004) Speeding-up pittsburgh learning classifier systems: modeling time and accuracy. In: Parallel Problem Solving from Nature—PPSN 2004, Springer, LNCS 3242, pp 1021–1031

Bacardit J, Stout M, Krasnogor N, Hirst JD, Blazewicz J (2006) Coordination number prediction using learning classifier systems: performance and interpretability. In: GECCO ’06: Proceedings of the 8th annual conference on genetic and evolutionary computation, ACM Press, pp 247–254

Bacardit J, Goldberg DE, Butz MV (2007a) Improving the performance of a pittsburgh learning classifier system using a default rule. In: Learning Classifier systems, revised selected papers of the international workshop on learning classifier systems 2003-2005, Springer-Verlag, LNCS 4399, pp 291–307

Bacardit J, Stout M, Hirst JD, Sastry K, Llorà X, Krasnogor N (2007b) Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In: GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, New York, NY, USA, pp 346–353. doi: 10.1145/1276958.1277033

Bernadó E, Llorà X, Garrell JM (2001) XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks. In: Fourth international workshop on learning classifier systems-IWLCS, pp 337–341

Blake C, Keogh E, Merz C (1998) UCI repository of machine learning databases. ( www.ics.uci.edu/mlearn/MLRepository.html )

Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123–140

Butz MV (2006) Rule-based evolutionary online learning systems: a principled approach to LCS analysis and design, studies in fuzziness and soft computing. Springer, Berlin, vol 109

Cantu-Paz E, Kamath C (2003) Inducing oblique decision trees with evolutionary algorithms. IEEE Trans Evol Comput 7(1): 54–68

Corcoran AL, Sen S (1994) Using real-valued genetic algorithms to evolve rule sets for classification. In: Proceedings of the IEEE conference on evolutionary computation, IEEE Press, pp 120–124. http://citeseer.nj.nec.com/corcoran94using.html

Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore

Cuff JA, Barton GJ (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 34: 508–519

De Jong KA, Spears WM (1991) Learning concept classification rules using genetic algorithms. In: Proceedings of the international joint conference on artificial intelligence, Morgan Kaufmann, pp 651–656

Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7: 1–30

Divina F, Marchiori E (2005) Handling continuous attributes in an evolutionary inductive learner. IEEE Trans Evol Comput 9(1): 31–43

Divina F, Keijzer M, Marchiori E (2003) A method for handling numerical attributes in GA-based inductive concept learners. In: GECCO 2003: Proceedings of the genetic and evolutionary computation conference, Springer, pp 898–908

Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, Berlin

Fürnkranz J (1999) Separate-and-conquer rule learning. Artif Intell Rev 13(1):3–54. http://citeseer.ist.psu.edu/26490.html

Giráldez R, Aguilar-Ruiz J, Riquelme J (2003) Natural coding: A more efficient representation for evolutionary learning. In: GECCO 2003: Proceedings of the genetic and evolutionary computation conference, Springer, pp 979–990

Giráldez R, Aguilar-Ruiz JS, Santos JCR (2005) Knowledge-based fast evaluation for evolutionary learning. IEEE Trans Syst Man Cybernet Part C 35(2): 254–261

Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182. http://portal.acm.org/citation.cfm?id=944968

John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence, Morgan Kaufmann Publishers, San Mateo, pp 338–345. http://citeseer.ist.psu.edu/john95estimating.html

Llorà X (2008) Personal communication

Llorà X, Garrell JM (2001) Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the third genetic and evolutionary computation conference, Morgan Kaufmann, pp 461–468

Llorà X, Sastry K (2006) Fast rule matching for learning classifier systems via vector instructions. In: GECCO ’06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM Press, New York, NY, USA, pp 1513–1520. doi: 10.1145/1143997.1144244

Llorà X, Priya A, Bhargava R (2008) Observer-invariant histopathology using genetics-based machine learning. Natural Computing, Special issue on Learning Classifier Systems p (in press)

Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Francisco

Rissanen J (1978) Modeling by shortest data description. Automatica 14: 465–471

Ruiz R (2007) New heuristics in feature selection for high dimensional data. AI Commun 20(2): 129–131

Stone C, Bull L (2003) For real! XCS with continuous-valued inputs. Evol Comput J 11(3): 298–336

Stout M, Bacardit J, Hirst JD, Krasnogor N (2008) Prediction of recursive convex hull class assignments for protein residues. Bioinformatics 24(7): 916–923

Vafaie H, De Jong KA (1992) Genetic algorithms as a tool for feature selection in machine learning. In: Proceeding of the 4th international conference on tools with artificial intelligence, pp 200–203

Venturini G (1993) Sia: A supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil PB (eds) Machine Learning: ECML-93, Proceedings of the European Conference on machine learning. Springer, Berlin, pp 280–296

Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2): 149–175

Wilson SW (1999) Get real! XCS with continuous-valued inputs. In: Booker L, Forrest S, Mitchell M, Riolo RL (eds) Festschrift in Honor of John H. Holland, Center for the Study of Complex Systems, pp 111–121. http://citeseer.nj.nec.com/233869.html

Witten IH, Frank E (2000) Data mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, San Francisco

Wood MJ, Hirst JD (2005) Protein secondary structure prediction with dihedral angles. Proteins 59: 476–481

Yang J, Honavar VG (1998) Feature subset selection using a genetic algorithm. IEEE Intell Syst 13(2): 44–49. doi: 10.1109/5254.671091