One-Class Classification: Taxonomy of Study and Review of Techniques
作者： Shehroz S.Khan , Michael G.Madden
DOI: 10.1017/S026988891300043X
关键词:
摘要: One-class classification (OCC) algorithms aim to build models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains learning of efficient classifiers by defining boundary just with knowledge positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection concept learning. In this paper we present a unified view general presenting taxonomy study for problems, which based on availability training data, used application domains applied. We further delve into each categories proposed comprehensive literature review algorithms, techniques methodologies focus their significance, limitations applications. conclude our discussing some open problems in field vision future research.
arxiv.org
128.84.21.199参考文章(119)
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