On the study of nearest neighbor algorithms for prevalence estimation in binary problems

DOI10.1016/j.patcog.2012.07.022zbMath1251.68176OpenAlexW2042295457WikidataQ62617992 ScholiaQ62617992MaRDI QIDQ1760417

Xianqiang Yang

Publication date: 13 November 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10651/28998

zbMATH Keywords

quantificationmethodologynearest neighborprevalence estimation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Quantification-oriented learning based on reliable classifiers

Uses Software

• UCI-ml
• kknn
• LIBSVM
• ML-KNN

Cites Work

• Unnamed Item
• Unnamed Item
• ML-KNN: A lazy learning approach to multi-label learning
• Locally linear reconstruction for instance-based learning
• On the application of ROC analysis to predict classification performance under varying class distributions
• A response to Webb and Ting's on the application of ROC analysis to predict classification performance under varying class distributions
• Choice of the smoothing parameter and efficiency of k-nearest neighbor classification
• Choosing k for two-class nearest neighbour classifiers with unbalanced classes
• Nearest neighbor pattern classification

This page was built for publication: On the study of nearest neighbor algorithms for prevalence estimation in binary problems