Abstract
In any Multi label classification problem, each instance is associated with multiple class labels. In this paper, we aim to predict the class labels of the test data accurately, using an improved multi label classification approach. This method is based on a framework that comprises an initial clustering phase followed by rule extraction using FP-Growth algorithm in label space. To predict the label of a new test data instance, this technique searches for the nearest cluster, thereby locating k-Nearest Neighbors within the corresponding cluster. The labels for the test instance are estimated by prior probabilities of the already predicted labels. Hence, by doing so, this scheme utilizes the advantages of the hybrid approach of both clustering and association rule mining.The proposed algorithm was tested on standard multi label datasets like yeast and scene. It achieved an overall accuracy of 81% when compared with scene dataset and a 68% in yeast dataset.
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References
Kommu, G.R., Trupthi, M., Pabboju, S.: A Novel approach for multi-label classification using probabilistic classifiers. In: IEEE International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), pp. 1–8 (2014)
Tao, G., Guiyang, L.: Improved conditional dependency networks for multi-label classification. In: Proceedings of the Seventh IEEE International Conference on Measuring Technology and Mechatronics Automation, pp. 561–565 (2015)
Yu, Y., Pedrycz, W., Miao, D., et al.: Neighborhood rough sets based multi-label classification for automatic image annotation. International Journal of Approximate Reasoning 54, 1373–1387 (2013). Elseiver
Li, J., Xu, J.: A fast multi-label classification algorithm based on double label support vector machine. In: IEEE International Conference on Computational Intelligence and Security (CIS 2009), vol. 2, pp. 30–35 (2009)
Nasierding, G., Sajjanhar, A.: Multi-label classification with clustering for image and text categorization. In: Proceedings of the Sixth IEEE International Conference on Image and Signal Processing (CISP), vol. 2, pp. 869–874 (2013)
Qin, F., Tang, X.-J., Cheng, Z.-K.: Application of apriori algorithm in multi-label classification. In: Proceedings of the Fifth IEEE International Conference on Computational and Information Sciences (ICCIS), pp. 717–720 (2013)
Chen, B., Hong, X., Duan, L., et al.: Improving multi-label classification performance by label constraints. In: Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–5 (2013)
Prajapati, P., Thakkar, A., Ganatra, A.: A Survey and Current Research Challenges in Multi-Label Classification Methods. International Journal of Soft Computing and Engineering (IJSCE) 2 (2012)
Fürnkranz, J., Hüllermeier, E., MencÃa, E.L., et al.: Multilabel classification via calibrated label ranking. The Journal of Machine Learning 73, 133–153 (2008). Springer
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. Proceedings of IEEE Transactions on Knowledge and Data Engineering, 406–417 (2007)
Zhang, M.-L., Zhou, Z.-H.: ML-KNN: A lazy learning approach to multi-label learning. The Journal of Pattern Recognition Society 40, 2038–2048 (2007). Elseiver
Li, B., Li, H., Wu, M., et al.: Multi-label classification based on association rules with application to scene classification. In: Proceedings of the Ninth IEEE International Conference for Young Computer Scientists (ICYCS), pp. 36–41 (2008)
Tahir, M.A., Kittler, J., Mikolajczyk, K., et al.: Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers. The Journal of Machine Learning 10, 11–21 (2010). Springer
Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Advances in Knowledge Discovery and Data Mining, pp. 22–30. Springer (2004)
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Prathibhamol, C.P., Ashok, A. (2016). Solving Multi Label Problems with Clustering and Nearest Neighbor by Consideration of Labels. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_43
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DOI: https://doi.org/10.1007/978-3-319-28658-7_43
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