This paper use multinomial nave Bayes to improve multi-label classification methods in a number of ways. First, we use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Second, we employ a co-training method to incorporate the dependencies among the class values. The results of our experiments show that the proposed approach outperforms other state-of-the-art methods.
AriasJ.GamezJ.A.NielsenT.D. and PuertaJ.M., A pairwise class interaction framework for multilabel classification, Probabilistic Graphical Models (2014), 17–32.
2.
BielzaC.LiG. and LarranagaP., Multidimensional classification with bayesian networks, International Journal of Approximate Reasoning52(6) (2011), 705–727.
3.
CaiL. and HofmannT., Hierarchical document categorization with support vector machines, in Proceedings of the 13th ACM International Conference on Information and Knowledge Management, 2004, pp. 78–87.
4.
Cesa-BianchiN.GentileC. and ZaniboniL., Hierarchical classification: Combining bayes with svm, in Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 177–184.
5.
DembczynskiK.ChengW. and HullermeierE., Bayes optimal multilabel classification via probabilistic classifier chains, Proceedings ICML, 2010.
6.
DembczyńskiK.WaegemanW.ChengW. and HllermeierE., On label dependence and loss minimization in multi-label classification, Machine Learning88 (2012), 5–45.
7.
FrankA. and AsuncionA., Uci machine learning repository, 2010.
8.
GhamrawiN. and McCallumA., Collective multi-label classification, in Inter. Conf. on Inform. and Know. Manage, 2005.
9.
GodboleS. and SarawagiS., Discriminative methods for multi-labeled classification, in The 8-th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004.
10.
HariharanB.Zelnik-ManorL.VishwanathanS.V.N. and VarmaM.. Large scale max-margin multi-label classification with priors, in Proceedings of the 27th International Conference on Machine Learning, 2010.
11.
KimS.HanK.RimH. and MyaengS., Some effective techniques for naïve bayes text classification, IEEE Transactions on Knowledge and Data Engineering18(11) (2006), 1457–1466.
12.
LeeC.-H.GutierrezF. and DouD., Calculating feature weights in naïve bayes with kullbackleibler measure, in 11th IEEE International Conference on Data Mining, 2011.
13.
Mulan. Mulan: A java library for multi-label learning, 2012.
14.
JinB.MullerB.ZhaiC. and LuX., Multi-label literature classification based on the gene ontology graph, Bioinformatics9(1) (2008).
15.
McCallumA. and NigamK., A comparison of event models for naive bayes text classification, in AAAI-98 Workshop on Learning for Text Categorization, 1998.
16.
McCallumA., Multi-label text classification with a mixture model trained by em, in AAAI99 Workshop on Text Learning, 1999.
17.
ReadJ.PfahringerB.HolmesG. and FrankE., Classifier chains for multi-label classification, ECML/PKDD (2009), 254–269.
18.
RennieJ.ShihL.TeevanJ. and KargerD., Tackling the poor assumptions of naive bayes text classifiers, in Proceedings of the 20th International Conference on Machine Learning (ICML), 2003, pp. 616–623.
19.
RodriguezJ. and LozanoJ., Multiple-objective learning of multi-dimensional bayesian classifiers, in Inter. Conf. on Hybrid Intelligent Systems, 2008.
20.
RousuJ.SaundersC.SzedmakS. and Shawe-TaylorJ., Learning hierarchical multi-category text classifcation models, in Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 774–751.
21.
SchneiderK.M., A new feature selection score for multinomial naïve bayes text classification based on kl-divergence. Proceedings of the 42nd Meeting of the Association of Computational Linguistics (ACL), 2004, 186–189.
22.
SchneiderK., Techniques for improving the performance of naïve bayes for text classification, LNCS3406 (2005), 682–693.
23.
SongS.-H. and LeeC.-H., Improving Multi-label Classification of Documents Using Fine-Grained Weights. in IEA/AIE 2015: The 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2015.
24.
SucarL.E.BielzaC.MoralesE.F.Hernandez-LealP.ZaragozaJ.H. and LarranagaP., Multilabel classification with Bayesian network-based chain classifiers, Pattern Recognition Letters41 (2014), 14–22.
25.
TunedIT. Tunedit data mining blog, 2012.
26.
de WaalP. and van der GaagL., Inference and learning in multi-dimensional bayesian network classifiers, in Proc. of Euro. Conf. on Symb. and Quant. Appr. to Reason. with Uncertain, 2007.
27.
ZhangM.-L. and ZhouZ.-H., A k-nearest neighbor based algorithm for multi-label classification, in Granular Computing, 2005 IEEE International Conference on, Vol 2, 2005, pp. 718–721.
28.
ZhangM.-L. and ZhangK., Multi-label learning by exploiting label dependency, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 999–1008.
