Abstract
Due to rapid growth in information technologies, millions of data are being generated at every time span. Data storage and processing are more expensive in terms of memory, resources, and time. The traditional machine learning classifier performs well when all the data is available along with the label information at the time of training, which may not be possible in every circumstances. Semi-supervised techniques overcome this limitation. With limited labeled data, semi-supervised learning makes use of both classification and clustering techniques for constructing efficient classifiers, and there is a demand to update the classifier periodically. Unlike traditional approaches, which use the entire data set for updating the classifier, our method updates in an incremental fashion. This paper proposes a framework for incremental classifiers in a semi-supervised environment. Experiments are conducted on benchmark datasets with the proposed approach and compared to the conventional method.
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References
Rahman, Md.G., ZahidulIslam, Md.: Adaptive decision forest: an incremental machine learning framework. Pattern Recognit. 122, 108345 (2022). https://doi.org/10.1016/j.patcog.2021.108345
Zhou, D.W., Yang, Y., Zhan, D.C.: Learning to classify with incremental new class. IEEE Trans. Neural Netw. Learn. Syst. 33(6), 2429–2443 (2022)
Zhang, S.S., Li, J.W., Zuo, X.: Adaptive online incremental learning for evolving data streams. Appl. Soft Comput. 105, 107255 (2021). https://doi.org/10.1016/j.asoc.2021.107255
Zhou, P., Wang, N., Zhao, S., Zhang, Y., Wu, X.: Difficult novel class detection in semi supervised streaming data. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3213682
Wang, F.Y., Zhou, D.W., Ye, H.J., Zhan, D.C.: FOSTER: feature boosting and compression for class-incremental learning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13685, pp. 398–414. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19806-9_23
Liao, G., et al.: A novel semi-supervised classification approach for evolving data streams. Expert Syst. Appl. 215, 119273 (2023). https://doi.org/10.1016/j.eswa.2022.119273. ISSN 0957-4174
Mesarcik, M., Ranguelova, E., Boonstra, A.J., Nieuwpoort, R.V.V.: Improving novelty detection using the reconstructions of nearest neighbours. Array 14, 100182 (2022) https://doi.org/10.1016/j.array.2022.100182. ISSN 2590-0056
Cai, Y., Zhexue Huang, J., Yin, J.: A new method to build the adaptive k-nearest neighbors similarity graph matrix for spectral clustering. Neurocomputing 493, 191–203 (2022). https://doi.org/10.1016/j.neucom.2022.04.030. ISSN 0925-2312
Gao, Y., Beijbom, O., Zhang, N., Darrell, T.: Compact bilinear pooling. In: Proceedings of CVPR, pp. 317–326 (2016)
Yang, Y., Zhu, N., Wu, Y., Cao, J., Zhan, D., Xiong, H.: A semi supervised attention model for identifying authentic sneakers. Big Data Min. Anal. 3(1), 29–40 (2020)
Bock, H.H.: Clustering methods: a history of k-means algorithms. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds.) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 161–172. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73560-1_15
Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 1–37 (2014)
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Gudapati, M., Swarupa Rani, K. (2023). Incremental Classifier in the Semi Supervised Learning Environment. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_21
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DOI: https://doi.org/10.1007/978-3-031-36402-0_21
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