Feature selection for density level-sets

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Authors

A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an efficient feature selection method for density level-set estimation where optimal kernel mixing coefficients and model parameters are determined simultaneously. Our approach generalizes one-class support vector machines and can be equivalently expressed as a semi-infinite linear program that can be solved with interleaved cutting plane algorithms. The experimental evaluation of the new method on network intrusion detection and object recognition tasks demonstrate that our approach not only attains competitive performance but also spares practitioners from a priori decisions on feature sets to be used.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsWray Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor
Number of pages13
Place of PublicationHeidelberg
PublisherSpringer Verlag
Publication date2009
Pages692-704
ISBN (print)978-3-642-04179-2
ISBN (electronic)978-3-642-04180-8
DOIs
Publication statusPublished - 2009
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases - 2009 - Bled, Slovenia
Duration: 07.09.200911.09.2009
https://www.k4all.org/event/european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery-in-databases/

    Research areas

  • Informatics - Concise representations, Cutting plane algorithms, Density levels, Efficient feature selections, Experimental evaluation, Feature selection, Feature sets, Linear programs, Mixing coefficient, Model parameters, Network intrusion detection, Observed data, One-class support vector machine, Semi-infinite
  • Business informatics

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