Abstract
In this paper, we propose a modified supervised adaptive resonance theory neural network, namely Fuzzy ARTMAP (FAM), to undertake multi-label data classification tasks. FAM is integrated with the binary relevance (BR) technique to form BR-FAM. The effectiveness of BR-FAM is evaluated using two benchmark multi-label data classification problems. Its results are compared with those other methods in the literature. The performance of BR-FAM is encouraging, which indicate the potential of FAM-based models for handling multi-label data classification tasks.
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References
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37, 1757–1771 (2004)
Tamaazousti, Y., Le Borgne, H., Popescu, A.: Constrained local enhancement of semantic features by content-based sparsity. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, ICME 2016, pp. 119–126. ACM, New York (2016)
Li, X., Huo, Y., Jin, Q., Xu., J.: Detecting violence in video using subclasses. In: Proceedings of the 2016 ACM on Multimedia Conference, MM 2016, pp. 586–590. ACM, Amsterdam (2016)
Chávez-Martínez, G., Ruiz-Correa, S., Gatica-Perez, D.: Happy and agreeable?: multi-label classification of impressions in social video. In: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia, MUM 2015, pp. 109–120. ACM, Austria (2015)
Lin, Y.-C., Yang, Y.-H., Chen, Homer H.: Exploiting online music tags for music emotion classification. ACM Trans. Multimedia Comput. Commun. Appl. 7 s, Article 26 (2011)
Yu, G., Rangwala, H., Domeniconi, C., Zhang, G., Yu, Z.: Protein Function Prediction with Incomplete Annotations. IEEE/ACM Trans. Comput. Biol. Bioinf. 11, 579–591 (2014)
Zhang, M.-L., Zhou, Z.-H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038–2048 (2007)
Xu, J.: An extended one-versus-rest support vector machine for multi-label classification. Neurocomputing 74, 3114–3124 (2011)
Xu, J.: Multi-label core vector machine with a zero label. Pattern Recogn. 47, 2542–2557 (2014)
Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3, 698–713 (1992)
Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. Lecture Notes in Artificial Intelligence 3056, 22–30 (2004)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3, 1–13 (2007)
Hüllermeier, E., Fürnkranz, J., Cheng, W., Bringer, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172, 1897–1916 (2008)
Fürnkranz, J., Hüllermeier, E., Mencia, L., Brinker, K.: Multi-label classification via calibrated label ranking. Mach. Learn. 73, 133–153 (2008)
Cherman, E.A., Monard, M.C., Metz, J.: Multi-label problem transformation methods: a case study. CLEI Electron. J. 14, 4 (2011)
Tanaka, E.A., Nozawa, S.R., Macedo, A.A., Baranauskas, J.A.: A multi-label approach using binary relevance and decision tree applied to functional genomics. J. Biomed. Inf. 53, 85–95 (2015)
Chou, S., Hsu, C.-L.: MMDT: a multi-valued and multi-labeled decision tree classifier for data mining. Expert Syst. Appl. 28, 799–812 (2005)
Wu, Q., Ye, Y., Zhang, H. Chow, Tommy W.S., Ho, S.-S.: ML-TREE: a tree-structure-based approach to multilabel learning. IEEE Trans. Neural Netw. Learn. Syst. 26, 430–443 (2015)
Chen, W.-J., Shao, Y.-H., Li, C.-N., Deng, N.-Y.: MLTSVM: a novel twin support vector machine to multi-label. Learning 52, 61–74 (2016)
Zhang, M.-L., Zhou, Z.-H.: Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18, 1338–1351 (2006)
Chen, Z., Chi, Z., Fu, H., Feng, D.: Multi-instance multi-label image classification: a neural approach. Neurocomputing 99, 298–306 (2013)
van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23, 1079–1089 (2011)
Yang, Y.: An evaluation of statistical approaches to text categorization. J. Inf. Retrieval 1, 78–88 (1999)
Mulan: a Java library for multi-label learning. http://mulan.sourceforge.net/datasets-mlc.html
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Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J. (2018). Fuzzy ARTMAP with Binary Relevance for Multi-label Classification. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_12
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DOI: https://doi.org/10.1007/978-3-319-59424-8_12
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