Abstract
Active learning has long been a research topic of machine learning. It aims to train a competitive classifier with a limited amount of labels. We design a novel approach in the study, which we refer to as active learning based on axiomatic fuzzy sets (AFS) and cost-sensitive classification. The classifier-based axiomatic fuzzy sets is employed as the benchmark classifier. The training data is transformed into some semantic rules for guiding the unlabeled instances to obtain class labels. To further improve the model’s classification accuracy in terms of the test instances, the cost-sensitive method and mutual k-nearest neighbors are employed to select important instances, and these instances are put into the training set until all test instances have been labeled. Thirteen UCI data sets are used in the experimental study. The results suggest that our designed method keeps a well semantic description for classifier. Additionally, the practicality and effectiveness of this method are verified in misclassification and total teacher costs.
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Acknowledgements
This work is supported by the university-industry collaborative education program (No. 201902139012), and the innovation and entrepreneurship training program for college students (No. 202010151583).
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Liu, Y., Guo, J., Li, S., Wang, L. (2021). Active Learning Method Based on Axiomatic Fuzzy Sets and Cost-Sensitive Classification. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_36
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