Abstract
The classification of objects based on classification codes is an important task for data processing in the field of official statistics. In our previous study, the supervised multiclass classifier was developed for autocoding, which has the advantages of simplicity and practical calculation time. However, the previous algorithm classified a few objects incorrectly. To address this problem, a new supervised multiclass classifier is proposed that extends the previously proposed classifier algorithm by applying the idea of partition coefficient or partition entropy. Numerical evaluation shows that the proposed algorithm has a better performance as compared to the previously proposed algorithm.
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We would like to thank Kaggle for making the Stack Overflow dataset available.
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Toko, Y., Wada, K., Iijima, S., Sato-Ilic, M. (2019). Supervised Multiclass Classifier for Autocoding Based on Partition Coefficient. In: Czarnowski, I., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Decision Technologies 2018. KES-IDT 2018 2018. Smart Innovation, Systems and Technologies, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-92028-3_6
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DOI: https://doi.org/10.1007/978-3-319-92028-3_6
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