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Original Articles

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

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Pages 63-74 | Received 12 Feb 2015, Accepted 27 Oct 2015, Published online: 14 Dec 2015

References

  • Bache, K., Lichman, M.: UCI machine learning repository (2013). URL http://archive.ics.uci.edu/ml
  • Chickering, D.M., Heckerman, D., Meek, C.: Large-sample learning of bayesian networks is np-hard. J. Mach. Learn. Res. 5(1), 1287–1330 (2004)
  • Frank, E., Hall, M., Pfahringer, B.: Locally weighted naive bayes. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, UAI'03, pp. 249–256 (2003)
  • Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997) doi: 10.1023/A:1007465528199
  • Guerriero, M., Svensson, L., Willett, P.: Bayesian data fusion for distributed target detection in sensor networks. Signal Processing, IEEE Transactions on 58(6), 3417–3421 (2010)
  • Hand, D.J., Till, R.J.: A simple generalisation of the area under the roc curve for multiple class classification problems. Mach. Learn. 45(2), 171–186 (2001) doi: 10.1023/A:1010920819831
  • Hernández-González, J., Inza, I.n., Lozano, J.A.: Learning bayesian network classifiers from label proportions. Pattern Recogn. 46(12), 3425–3440 (2013) doi: 10.1016/j.patcog.2013.05.002
  • Huang, J., Lu, J., Ling, C.X.: Comparing naive bayes, decision trees, and svm with auc and accuracy. In: Proceedings of the 3rd IEEE International Conference on Data Mining, ICDM ’03, pp. 553–556 (2003)
  • Jiang, L., Cai, Z., Wang, D., Zhang, H.: Improving tree augmented naive bayes for class probability estimation. Knowledge-Based Systems 26(0), 239–245 (2012) doi: 10.1016/j.knosys.2011.08.010
  • Jiang, L., Cai, Z., Zhang, H., Wang, D.: Naive bayes text classifiers: a locally weighted learning approach. J. Exp. Theor. Artif. Intell. 25(2), 273–286 (2013) doi: 10.1080/0952813X.2012.721010
  • Jiang, L., Wang, D., Cai, Z.: Discriminatively weighted naive bayes and its application in text classification. International Journal on Artificial Intelligence Tools 21(1), 1–19 (2012) doi: 10.1142/S0218213011004770
  • Jiang, L., Zhang, H.: Weightily averaged one-dependence estimators. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI'06, pp. 970–974 (2006)
  • Jiang, L., Zhang, H., Cai, Z.: A novel bayes model: Hidden naive bayes. IEEE Trans. on Knowl. and Data Eng. 21(10), 1361–1371 (2009) doi: 10.1109/TKDE.2008.234
  • Jiang, L., Zhang, H., Cai, Z., Wang, D.: Weighted average of one-dependence estimators. Journal of Experimental and Theoretical Artificial Intelligence 24(2), 219–230 (2012) doi: 10.1080/0952813X.2011.639092
  • Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers:a decision-tree hybrid. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, KDD ’96, pp. 202–207 (1996)
  • Langley, P., Sage, S.: Induction of selective bayesian classifiers. In: Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence, UAI ’94, pp. 339–406 (1994)
  • Ling, C.X., Huang, J., Zhang, H.: Auc: A statistically consistent and more discriminating measure than accuracy. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI ’03, pp. 519–524 (2003)
  • Ling, C.X., Yan, R.J.: Decision tree with better ranking. In: Proceedings of the 20th International Conference on Machine Learning, ICML ’03, pp. 480–487. Morgan Kaufmann (2003)
  • Liu, W.Y., Yue, K., Li, W.H.: Constructing the bayesian network structure from dependencies implied in multiple relational schemas. Expert Syst. Appl. 38(6), 7123–7134 (2011) doi: 10.1016/j.eswa.2010.12.053
  • Needham, C.J., Bradford, J.R., Bulpitt, A.J., West-head, D.R.: A primer on learning in bayesian networks for computational biology. PLoS Computational Biology 3(8), 1409–1416 (2007) doi: 10.1371/journal.pcbi.0030129
  • Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(8), 831–843 (2000)
  • Pan, S., Wu, J., Zhu, X.: Cogboost: Boosting for fast cost-sensitive graph classification. Knowledge and Data Engineering, IEEE Transactions on PP(99), 1–1 (2015). DOI 10.1109/TKDE.2015.2391115
  • Pan, S., Wu, J., Zhu, X., Long, G., Zhang, C.: Finding the best not the most: regularized loss minimization subgraph selection for graph classification. Pattern Recognition (2015). DOI http://dx.doi.org/10.1016/j.patcog.2015.05.019
  • Pan, S., Wu, J., Zhu, X., Zhang, C.: Graph ensemble boosting for imbalanced noisy graph stream classification. Cybernetics, IEEE Transactions on 45(5), 940–954 (2015)
  • Provost, F., Domingos, P.: Tree induction for probability-based ranking. Mach. Learn. 52(3), 199–215 (2003) doi: 10.1023/A:1024099825458
  • Su, J., Zhang, H.: Full bayesian network classifiers. In: Proceedings of the 23rd International Conference on Machine Learning, ICML ’06, pp. 897–904 (2006)
  • Umut Orhan, K.A., Comert, O.: Least squares approach to locally weighted naive bayes method. Journal of New Results in Science. 1(1), 71–80 (2012)
  • Wang, B., Zhang, H.: Probability based metrics for locally weighted naive bayes. In: Proceedings of the 20th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence, CAI ’07, pp. 180–191 (2007)
  • Webb, G.I., Boughton, J.R., Wang, Z.: Not so naive bayes: Aggregating one-dependence estimators. Machine Learning 58(1), 5–24 (2005) doi: 10.1007/s10994-005-4258-6
  • Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers, San Francisco, CA (2005). URL http://www.cs.waikato.ac.nz/ml/weka/
  • Wu, J., Cai, Z.: Attribute weighting via differential evolution algorithm for attribute weighted naive bayes (wnb). Journal of Computational Information Systems 7(5), 1672–1679 (2011)
  • Wu, J., Cai, Z.: Boosting for superparentonedependence estimators. International Journal of Computing Science and Mathematics 4(3), 277–286 (2013) doi: 10.1504/IJCSM.2013.057257
  • Wu, J., Cai, Z.: A naive bayes probability estimation model based on self-adaptive differential evolution. Journal of Intelligent Information Systems 42(3), 671–694 (2013) doi: 10.1007/s10844-013-0279-y
  • Wu, J., Cai, Z., Ao, S.: Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification. International Journal of Computer Applications in Technology 43(4), 378–384 (2012) doi: 10.1504/IJCAT.2012.047164
  • Wu, J., Cai, Z., Chen, X., Ao, S.: Active aode learning based on a novel sampling strategy and its application. International Journal of Computer Applications in Technology 47(4), 326–333 (2013) doi: 10.1504/IJCAT.2013.055325
  • Wu, J., Cai, Z., Zeng, S., Zhu, X.: Artificial immune system for attribute weighted naive bayes classification. In: Proceedings of the 26th International Joint Conference on Neural Networks, IJCNN ’13, pp. 798–805 (2013)
  • Wu, J., Cai, Z., Zhu, X.: Self-adaptive probability estimation for naive bayes classification. In: Proceedings of the 26th International Joint Conference on Neural Networks, IJCNN ’13, pp. 2303–2310 (2013)
  • Wu, J., Cai, Z.h.: Learning attribute weighted aode for roc area ranking. Int. J. Inf. Commun. Techol. 6(1), 23–38 (2014) doi: 10.1504/IJICT.2014.057970
  • Wu, J., Pan, S., Zhu, X., Cai, Z.: Boosting for multigraph classification. Cybernetics, IEEE Transactions on 45(3), 430–443 (2015)
  • Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., Zhang, C.: Self-adaptive attribute weighting for naive bayes classification. Expert Syst. Appl. 42(3), 1487–1502 (2015) doi: 10.1016/j.eswa.2014.09.019
  • Wu, J., Zhu, X., Zhang, C., Yu, P.: Bag constrained structure pattern mining for multi-graph classification. Knowledge and Data Engineering, IEEE Transactions on 26(10), 2382–2396 (2014)
  • Zaidi, N.A., Cerquides, J., Carman, M.J., Webb, G.I.: Alleviating naive bayes attribute independence assumption by attribute weighting. Journal of Machine Learning Research 14(1), 1947–1988 (2013)
  • Zhang, H., Jiang, L., Su, J.: Hidden naive bayes. In: Proceedings of the 20th national conference on Artificial intelligence, AAAI ’05, pp. 919–924 (2005)
  • Zhang, H., Sheng, S.: Learning weighted naive bayes with accurate ranking. In: Proceedings of the 4th International Conference on Data Mining, ICDM ’04, pp. 567–570 (2004)
  • Zhang, H., Su, J.: Naive bayesian classifiers for ranking. In: Proceedings of the 15th European Conference on Machine Learning, ECML ’04, pp. 501–512 (2004)
  • Zhang, L., Ji, Q.: A bayesian network model for automatic and interactive image segmentation. Image Processing, IEEE Transactions on 20(9), 2582–2593 (2011)
  • Zhao, J., Liu, J., Sun, Y., Sun, Z.: Tree augmented naive possibilistic network classifier. In: Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD ’11, pp. 1065–1069 (2011)

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