메뉴 건너뛰기




Volumn 5, Issue 2, 2012, Pages 87-102

Analysing BioHEL using challenging boolean functions

Author keywords

Evolutionary algorithms; Large scale datasets; Learning classifier systems; Rule induction

Indexed keywords

BREAKPOINT; CLASS IMBALANCE; DEFAULT RULE; EMPIRICAL ANALYSIS; FITNESS FUNCTIONS; GENETICS BASED MACHINE LEARNING; HIGHLY SENSITIVE; IDEAL SOLUTIONS; LARGE-SCALE DATASETS; LEARNING CLASSIFIER SYSTEM; MACHINE LEARNING TECHNIQUES; NORMAL FORM; RULE INDUCTION;

EID: 84861639102     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-012-0080-9     Document Type: Article
Times cited : (7)

References (29)
  • 2
    • 65149084519 scopus 로고    scopus 로고
    • Improving the scalability of rule-based evolutionary learning
    • doi: 10. 1007/s12293-008-0005-4
    • Bacardit J, Burke E, Krasnogor N (2009) Improving the scalability of rule-based evolutionary learning. Memetic Computing 1(1): 55-67. doi: 10. 1007/s12293-008-0005-4.
    • (2009) Memetic Computing , vol.1 , Issue.1 , pp. 55-67
    • Bacardit, J.1    Burke, E.2    Krasnogor, N.3
  • 3
    • 32444433247 scopus 로고    scopus 로고
    • Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system
    • Bacardit J, Garrell JM (2003) Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Proceedings of the 6th International Workshop on Learning Classifier Systems.
    • (2003) Proceedings of the 6th International Workshop On Learning Classifier Systems
    • Bacardit, J.1    Garrell, J.M.2
  • 5
    • 33745803301 scopus 로고    scopus 로고
    • Speeding-Up pittsburgh learning classifier systems: modeling time and accuracy
    • In: Parallel problem solving from nature-PPSN VIII, chap. 103. Springer, Berlin, Heidelberg
    • Bacardit J, Goldberg DE, Butz MV, Llorá X, Garrell JM (2004) Speeding-Up pittsburgh learning classifier systems: modeling time and accuracy. In: Parallel problem solving from nature-PPSN VIII, Lecture Notes in Computer Science, vol. 3242, chap. 103. Springer, Berlin, Heidelberg, pp 1021-1031. http://www. springerlink. com/content/66w8u56a61wntqa6.
    • (2004) Lecture Notes in Computer Science , vol.3242 , pp. 1021-1031
    • Bacardit, J.1    Goldberg, D.E.2    Butz, M.V.3    Llorá, X.4    Garrell, J.M.5
  • 7
    • 72749106564 scopus 로고    scopus 로고
    • A mixed discrete-continuous attribute list representation for large scale classification domains
    • ACM Press, New York, NY. doi: 10. 1145/1569901. 1570057
    • Bacardit J, Krasnogor N (2009) A mixed discrete-continuous attribute list representation for large scale classification domains. In: GECCO '09: Proceedings of the 11th annual conference on genetic and evolutionary computation, pp 1155-1162. ACM Press, New York, NY. doi: 10. 1145/1569901. 1570057.
    • (2009) GECCO '09: Proceedings of the 11th Annual Conference On Genetic and Evolutionary Computation , pp. 1155-1162
    • Bacardit, J.1    Krasnogor, N.2
  • 10
    • 80054969308 scopus 로고    scopus 로고
    • Functional network construction in arabidopsis using rule-based machine learning on large-scale data sets
    • doi:10.1105/tpc.111.088153
    • Bassel GW, Glaab E, Marquez J, Holdsworth MJ, Bacardit J (2011) Functional network construction in arabidopsis using rule-based machine learning on large-scale data sets. Plant Cell Online 23(9): 3101-3116. doi: 10. 1105/tpc. 111. 088153.
    • (2011) Plant Cell Online , vol.23 , Issue.9 , pp. 3101-3116
    • Bassel, G.W.1    Glaab, E.2    Marquez, J.3    Holdsworth, M.J.4    Bacardit, J.5
  • 12
    • 33750249654 scopus 로고    scopus 로고
    • Studying XCS/BOA learning in boolean functions: Structure encoding and random boolean functions
    • ACM, New York, NY, doi: 10. 1145/1143997. 1144236
    • Butz MV, Pelikan M (2006) Studying XCS/BOA learning in boolean functions: structure encoding and random boolean functions. In: GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, New York, NY, pp 1449-456. doi: 10. 1145/1143997. 1144236.
    • (2006) GECCO '06: Proceedings of the 8th Annual Conference On Genetic and Evolutionary Computation , pp. 1449-1456
    • Butz, M.V.1    Pelikan, M.2
  • 19
    • 0040456384 scopus 로고
    • Learning concept classification rules using genetic algorithms
    • Morgan Kaufmann Publishers Inc., Sydney, New South Wales
    • Jong KD, Spears WM (1991) Learning concept classification rules using genetic algorithms. In: Proceedings of the 12th international joint conference on Artificial intelligence, vol 2. Morgan Kaufmann Publishers Inc., Sydney, New South Wales, pp 651-656. http://portal. acm. org/citation. cfm?id=1631559.
    • (1991) Proceedings of the 12th international joint conference on Artificial intelligence , vol.2 , pp. 651-656
    • Jong, K.D.1    Spears, W.M.2
  • 21
    • 55549116330 scopus 로고    scopus 로고
    • Evolutionary rule-based systems for imbalanced data sets
    • Orriols-Puig A, Bernadó-Mansilla E (2008) Evolutionary rule-based systems for imbalanced data sets. Soft Comput 13(3): 213-225. http://portal. acm. org/citation. cfm?id=1459244.
    • (2008) Soft Comput , vol.13 , Issue.3 , pp. 213-225
    • Orriols-Puig, A.1    Bernadó-Mansilla, E.2
  • 23
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • Rissanen J (1978) Modeling by shortest data description. Automatica 14: 465-471.
    • (1978) Automatica , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 24
    • 0043284110 scopus 로고    scopus 로고
    • For real! XCS with continuous-valued inputs
    • doi: 10. 1162/106365603322365315
    • Stone C, Bull L (2003) For real! XCS with continuous-valued inputs. Evol Comput 11: 299-336. doi: 10. 1162/106365603322365315.
    • (2003) Evol Comput , vol.11 , pp. 299-336
    • Stone, C.1    Bull, L.2
  • 25
    • 41349105501 scopus 로고    scopus 로고
    • Prediction of recursive convex hull class assignments for protein residues
    • doi: 10. 1093/bioinformatics/btn050
    • Stout M, Bacardit J, Hirst JD, Krasnogor N (2008) Prediction of recursive convex hull class assignments for protein residues. Bioinformatics 24(7): 916-923. doi: 10. 1093/bioinformatics/btn050. http://bioinformatics. oxfordjournals. org/cgi/.
    • (2008) Bioinformatics , vol.24 , Issue.7 , pp. 916-923
    • Stout, M.1    Bacardit, J.2    Hirst, J.D.3    Krasnogor, N.4
  • 26
    • 84971641220 scopus 로고
    • SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts
    • In: Brazdil PB, Springer, New York
    • 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, New York, pp 280-296.
    • (1993) Machine Learning: ECML-93-Proceedings of the European Conference On Machine Learning , pp. 280-296
    • Venturini, G.1
  • 27
    • 0001387704 scopus 로고
    • Classifier fitness based on accuracy
    • doi: 10. 1162/evco. 1995. 3. 2. 149
    • Wilson SW (1995) Classifier fitness based on accuracy. Evol Comput 3(2): 149-175. doi: 10. 1162/evco. 1995. 3. 2. 149.
    • (1995) Evol Comput , vol.3 , Issue.2 , pp. 149-175
    • Wilson, S.W.1
  • 28
    • 84942897286 scopus 로고    scopus 로고
    • Mining oblique data with XCS
    • Luca Lanzi P, Stolzmann W, Wilson S (eds), Springer, Berlin/Heidelberg, doi: 10. 1007/3-540-44640-0_11
    • Wilson SW (2001) Mining oblique data with XCS. In: Luca Lanzi P, Stolzmann W, Wilson S (eds), Advances in learning classifier systems, lecture notes in computer science, vol 1996. Springer, Berlin/Heidelberg, pp 283-290. doi: 10. 1007/3-540-44640-0_11.
    • (2001) Advances In Learning Classifier Systems, Lecture Notes In Computer Science , vol.1996 , pp. 283-290
    • Wilson, S.W.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.