Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks

Expert Systems with Applications - Tập 41 - Trang 1937-1946 - 2014
Dewan Md. Farid1, Li Zhang1, Chowdhury Mofizur Rahman2, M.A. Hossain1, Rebecca Strachan1
1Computational Intelligence Group, Department of Computer Science and Digital Technology, Northumbria University, Newcastle upon Tyne, UK
2Department of Computer Science & Engineering, United International University, Bangladesh

Tài liệu tham khảo

Aitkenhead, 2008, A co-evolving decision tree classification method, Expert Systems with Applications, 34, 18, 10.1016/j.eswa.2006.08.008 Aviad, 2011, Classsification by clustering decision tree-like classifier based on adjusted clusters, Expert Systems with Applications, 38, 8220, 10.1016/j.eswa.2011.01.001 Balamurugan, 2009, Effective solution for unhandled exception in decision tree induction algorithms, Expert Systems with Applications, 36, 12113, 10.1016/j.eswa.2009.03.072 Breiman, 1984 Bujlow, T., Riaz, M.T., & Pedersen, J.M. (2012). A method for classification of network traffic based on C5.0 machine learning algorithm. In Proc. of the International Conference on Computing, Networking and Communications (ICNC) (pp. 237–241). Chandra, 2011, Robust approach for estimating probabilities in naïve Bayesian classifier for gene expression data, Expert Systems with Applications, 38, 1293, 10.1016/j.eswa.2010.06.076 Chandra, 2009, Moving towards efficient decision tree construction, Information Sciences, 179, 1059, 10.1016/j.ins.2008.12.006 Chandra, 2009, Fuzzifying gini index based decision trees, Expert Systems with Applications, 36, 8549, 10.1016/j.eswa.2008.10.053 Chen, 2009, Feature selection for text classification with naïve Bayes, Expert Systems with Applications, 36, 5432, 10.1016/j.eswa.2008.06.054 Chen, 2009, Using decision trees to summarize associative classification rules, Expert Systems with Applications, 36, 2338, 10.1016/j.eswa.2007.12.031 Fan, 2010, Partition-conditional ICA for Bayesian classification of microarray data, Expert Systems with Applications, 37, 8188, 10.1016/j.eswa.2010.05.068 Farid, 2010, Combining naïve Bayes and decision tree for adaptive intrusion detection, International Journal of Network Security & Its Applications, 2, 12, 10.5121/ijnsa.2010.2202 Farid, 2010, Anomaly network intrusion detection based on improved self adaptive Bayesian algorithm, Journal of Computers, 5, 23, 10.4304/jcp.5.1.23-31 Farid, 2011, Adaptive intrusion detection based on boosting and naïve Bayesian classifier, International Journal of Computer Applications, 24, 12, 10.5120/2932-3883 Farid, 2013, An adaptive ensemble classifier for mining concept drifting data streams, Expert Systems with Applications, 40, 5895, 10.1016/j.eswa.2013.05.001 Franco-Arcega, 2011, Decision tree induction using a fast splitting attribute selection for large datasets, Expert Systems with Applications, 38, 14290 Frank, A., & Asuncion, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 29.07.2013. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The weka data mining software: An update. SIGKDD Explorations, 11. http://www.cs.waikato.ac.nz/ml/weka/. Accessed 29.07.2013. Hsu, 2008, Extended naïve Bayes classifier for mixed data, Expert Systems with Applications, 35, 1080, 10.1016/j.eswa.2007.08.031 Jamain, 2008, Mining supervised classification performance studies: A meta-analytic investigation, Journal of Classification, 25, 87, 10.1007/s00357-008-9003-y Koc, 2012, A network intrusion detection system based on a hidden naïve Bayes multiclass classifier, Expert Systems with Applications, 39, 13492, 10.1016/j.eswa.2012.07.009 Lee, 2010, Automatically computed document dependent weighting factor facility for na”ıve Bayes classification, Expert Systems with Applications, 37, 8471, 10.1016/j.eswa.2010.05.030 Liao, 2012, Data mining techniques and applications – a decade review from 2000 to 2011, Expert Systems with Applications, 39, 11303, 10.1016/j.eswa.2012.02.063 Loh, 1997, Split selection methods for classification tree, Statistica Sininca, 7, 815 McHugh, 2000, Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory, ACM Transaction on Information and System Security, 3, 262, 10.1145/382912.382923 Ngai, 2009, Application of data mining techniques in customer relationship management: A literature review and classification review article, Expert Systems with Applications, 36, 2592, 10.1016/j.eswa.2008.02.021 Polat, 2009, A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems, Expert Systems with Applications, 36, 1587, 10.1016/j.eswa.2007.11.051 Quinlan, 1986, Induction of decision tree, Machine Learning, 1, 81, 10.1007/BF00116251 Quinlan, 1993 Safavian, 1991, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man and Cybernetics, 21, 660, 10.1109/21.97458 Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A.A. (2009). A detailed analysis of the KDD CUP 99 data set. In Proc. of the 2nd IEEE Int. Conf. on Computational Intelligence in Security and Defense Applications (pp. 53–58). Turney, 1995, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 369, 10.1613/jair.120 Utgoff, 1989, Incremental induction of decision trees, Machine Learning, 4, 161, 10.1023/A:1022699900025 Utgoff, P.E. (1988). ID5: An incremental ID3. In Proc. of the fifth National Conference on Machine Learning, Ann Arbor, Michigan, USA (pp. 107–120). Valle, 2012, Job performance prediction in a call center using a naïve Bayes classifier, Expert Systems with Applications, 39, 9939, 10.1016/j.eswa.2011.11.126