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