Spherical Classification of Data, a New Rule-Based Learning Method
- Authors
- Ma, Zhengyu; Ryoo, Hong Seo
- Issue Date
- 2021
- Publisher
- SPRINGER
- Keywords
- Supervised learning; Classification; Spherical pattern; Rule induction
- Citation
- JOURNAL OF CLASSIFICATION, v.38, no.1, pp.44 - 71
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- JOURNAL OF CLASSIFICATION
- Volume
- 38
- Number
- 1
- Start Page
- 44
- End Page
- 71
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/130289
- DOI
- 10.1007/s00357-019-09355-z
- ISSN
- 0176-4268
- Abstract
- This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new data. Numerical studies with public machine learning datasets from Lichman (2013), in comparison with well-established classification methods from Boros et al. (IEEE Transactions on Knowledge and Data Engineering, 12, 292-306, 2000) and Waikato Environment for Knowledge Analysis (), demonstrate the aforementioned utilities of the new method well.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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