Abstract
The classification of objects based on corresponding classes is an important task in official statistics. In the previous study, the overlapping classifier that assigns classes to an object based on the reliability score was proposed. The proposed reliability score has been defined considering both the uncertainty from data and the uncertainty from the latent classification structure in data and generalized using the idea of the T-norm in statistical metric space. This paper proposes a new procedure for the improvement of the training dataset based on a pattern of reliability scores to get a better classification accuracy. The numerical example shows the proposed procedure gives a better result as compared to the result of our previous study.
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Toko, Y., Sato-Ilic, M. (2020). Improvement of the Training Dataset for Supervised Multiclass Classification. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_25
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DOI: https://doi.org/10.1007/978-981-15-5925-9_25
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