No abstract available.
Cited By
- Uwano F, Dobashi K, Takadama K and Kovacs T Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining Proceedings of the Genetic and Evolutionary Computation Conference Companion, (1465-1472)
- Lanzi P Learning classifier systems Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, (407-430)
- Kovacs T and Tindale R Analysis of the niche genetic algorithm in learning classifier systems Proceedings of the 15th annual conference on Genetic and evolutionary computation, (1069-1076)
- Marzukhi S, Browne W and Zhang M Adaptive artificial datasets through learning classifier systems for classification tasks Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, (1243-1250)
- Marzukhi S, Browne W and Zhang M Two-cornered learning classifier systems for pattern generation and classification Proceedings of the 14th annual conference on Genetic and evolutionary computation, (895-902)
- Kovacs T, Edakunni N and Brown G Accuracy exponentiation in UCS and its effect on voting margins Proceedings of the 13th annual conference on Genetic and evolutionary computation, (1251-1258)
- Butz M Learning classifier systems Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, (941-962)
- Butz M Learning classifier systems Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, (2331-2352)
- Alcala-Fdez J, Flugy-Pape N, Bonarini A and Herrera F (2010). Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules, Fundamenta Informaticae, 98:1, (1-14), Online publication date: 1-Jan-2010.
- Mucientes M and Bugarín A (2010). People detection through quantified fuzzy temporal rules, Pattern Recognition, 43:4, (1441-1453), Online publication date: 1-Apr-2010.
- Santos M, Mathew W and Santos H GridclassTK Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science, (43-47)
- Romero C, González P, Ventura S, del Jesus M and Herrera F (2009). Evolutionary algorithms for subgroup discovery in e-learning, Expert Systems with Applications: An International Journal, 36:2, (1632-1644), Online publication date: 1-Mar-2009.
- Li M and Wang Z (2009). A hybrid coevolutionary algorithm for designing fuzzy classifiers, Information Sciences: an International Journal, 179:12, (1970-1983), Online publication date: 1-May-2009.
- Santos M, Mathew W, Kovacs T and Santos H (2009). A grid data mining architecture for learning classifier systems, WSEAS Transactions on Computers, 8:5, (820-830), Online publication date: 1-May-2009.
- Butz M Learning classifier systems Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, (2367-2388)
- Kovacs T and Bull L Toward a better understanding of rule initialisation and deletion Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, (2777-2780)
- Marshall J, Brown G and Kovacs T Bayesian estimation of rule accuracy in UCS Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, (2831-2834)
- Butz M Learning classifier systems Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, (3035-3056)
- Brown G, Kovacs T and Marshall J UCSpv Proceedings of the 9th annual conference on Genetic and evolutionary computation, (1774-1781)
- Sigaud O and Wilson S (2007). Learning classifier systems: a survey, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 11:11, (1065-1078), Online publication date: 1-Sep-2007.
- Berlanga F, del Jesus M, Gacto M and Herrera F A genetic-programming-based approach for the learning of compact fuzzy rule-based classification systems Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing, (182-191)
- Kovacs T and Kerber M (2006). A Study of Structural and Parametric Learning in XCS, Evolutionary Computation, 14:1, (1-19), Online publication date: 1-Mar-2006.
- Marshall J and Kovacs T A representational ecology for learning classifier systems Proceedings of the 8th annual conference on Genetic and evolutionary computation, (1529-1536)
- Lanzi P and Loiacono D Standard and averaging reinforcement learning in XCS Proceedings of the 8th annual conference on Genetic and evolutionary computation, (1489-1496)
Index Terms
- Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Recommendations
Ensembling Base Classifiers to Improve Predictive Accuracy
DCABES '15: Proceedings of the 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)The algorithm of ensembling base classifiers can improve predictive accuracy, and achieve a better generalization. However, the ensemble classification methods in literature have been used in more rule-based algorithms of classifier. This paper presents ...
Improve Flow Accuracy and Byte Accuracy in Network Traffic Classification
ICIC '08: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial IntelligenceMost of the current network traffic classification approaches employ single classifier method with achieving lower accuracy under small training set. Different from high flow accuracy, byte accuracy, as an important metric for network traffic ...
A Comparative Study of Selected Classifiers with Classification Accuracy in User Profiling
CSIE '09: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 03In recent years the use of personalized service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. In literature a number of classification algorithms have been used to ...