Batch and online learning algorithms for nonconvex neyman-pearson classification | Zendy

Gilles Gasso | Zendy; Aristidis Pappaioannou | Zendy; M. A. Spivak | Zendy; Léon Bottou | Zendy

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Batch and online learning algorithms for nonconvex neyman-pearson classification

Author(s) -

Gilles Gasso,

Aristidis Pappaioannou,

M. A. Spivak,

Léon Bottou

Publication year - 2011

Publication title -

acm transactions on intelligent systems and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.914

H-Index - 63

eISSN - 2157-6912

pISSN - 2157-6904

DOI - 10.1145/1961189.1961200

Subject(s) - computer science , ranking (information retrieval) , constraint (computer aided design) , algorithm , artificial intelligence , machine learning , bipartite graph , scale (ratio) , theoretical computer science , mathematics , graph , geometry , physics , quantum mechanics

International audienceWe describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large scale datasets. Empirical evidences illustrate the potential of the proposed methods

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