Hybrid ensemble learning approach for generation of classification rules

Proceedings - International Conference on Machine Learning and Cybernetics

Volumn 1, Issue , 2015, Pages 377-382

  Liu, Han ^a   Gegov, Alexander ^a   Cocea, Mihaela ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

Data mining; Ensemble learning; If then rules; Inductive learning; Machine learning; Rule based classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CYBERNETICS; DATA HANDLING; DATA MINING; DECISION TREES; LEARNING SYSTEMS;

ENSEMBLE LEARNING; ENSEMBLE LEARNING APPROACH; IF-THEN RULES; INDUCTIVE LEARNING; INTELLIGENT PROCESSING; MACHINE LEARNING PROBLEM; RULE-BASED CLASSIFICATION; RULE-BASED CLASSIFIER;

LEARNING ALGORITHMS;

EID: 84955347120     PISSN: 2160133X     EISSN: 21601348     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2015.7340951     Document Type: Conference Paper

References (15)

1. J. R. Quinlan, C4.5: programs for machine learning. Morgan Kaufman, San Francisco, CA, USA, 1993.
2. J. Furnkranz, Separate-and-Conquer rule learning, Artificial Intelligence Review, Vol. 13, pp. 3-54,1999.
3. P. N. Tang, M. Steinbach and V. Kumar, Introduction to Data Mining. New Jersey: Pearson Education, 2006.
4. I. Kononenko and M. Kukar, Machine Learning and Data Mining. Chichester, West Sussex: Horwood Publishing Limmited, 2007.
5. D. Brain, Learning From Large Data: Bias, Variance, Sampling, and Learning Curves. PhD Thesis, Deakin University, 2003.
6. L. Breiman, Bagging Predictors, Machine Learning, Vol. 2, No. 24,pp.123-140,1996.
7. L. Breiman, Random Forests. Machine Learning, Vol. 45, No. l,pp. 5-32,2001.
8. J. Li and L. Wong, Rule-Based Data Mining Methods for Classification Problems in Biomedical Domains. In: 1 h European Conference on Machine Learning and h European Conference on Principles and Practice for of Knowledge Discovery in Databases, Pisa, 2004.
9. H. Liu and A. Gegov, Collaborative Decision Making by Ensemble Rule Based Classification Systems. In: W. Pedrycz and S. M. Chen, Granular Computing and Decision Making, Studies in Big Data, Vol. 10, pp. 245-264, Springer, 2015.
10. P. Smyth and R. M. Goodman, An Information Theoretic Approach to Rule Induction from Databases. IEEE Transactions on Knowledge and Data Engineering, Vol. 4, pp. 301-316,1992.
11. P. N. Tan, V. Kumar and J. Srivastava, Selecting the right objective measure for association analysis. Information Systems, Vol. 29, pp. 293-313,2004.
12. L. Geng and H. J. Hamilton, Interestingness measures for data mining: A survey. ACM Computing Surveys, Vol. 38, No. 3,2006.
13. M. Lichman, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.
14. J. Cendrowska, PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, Vol. 27, pp. 349-370,1987.
15. H. Liu, A. Gegov and F. Stahl, Unified framework for construction of rule based classification systems. In: W. Pedrycz and S. M. Chen, Information Granularity, Big Data and Computational Intelligence, Studies in Big Data, Vol. 8, pp. 209-230, Springer, 2015.