Discovering useful concept prototypes for classification based on filtering and abstraction
Publication in refereed journal


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其它資訊
摘要The nearest-neighbor algorithm and its derivatives have been shown to perform well for pattern classification. Despite their high classification accuracy, they suffer from high storage requirement, computational cost, and sensitivity to noise. We develop a new framework, called ICPL (Integrated Concept Prototype Learner), which integrates instance-filtering and instance-abstraction techniques by maintaining a balance of different kinds of concept prototypes according to instance locality. The abstraction component, based on typicality, employed in our ICPL framework is specially designed for concept integration. We have conducted experiments on a total of 50 real-world benchmark data sets. We find that our ICPL framework maintains or achieves better classification accuracy and gains a significant improvement in data reduction compared with existing filtering and abstraction techniques as well as some existing techniques.
著者Lam W, Keung CK, Liu DY
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份2002
月份8
日期1
卷號24
期次8
出版社IEEE COMPUTER SOC
頁次1075 - 1090
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言英式英語
關鍵詞classification; data mining; instance abstraction; machine learning; prototype learning
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2021-29-11 於 00:26