Standard
Feature selection for density level-sets. / Kloft, Marius; Nakajima, Shinichi
; Brefeld, Ulf.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Hrsg. / Wray Buntine; Marko Grobelnik; Dunja Mladenic; John Shawe-Taylor. Heidelberg: Springer, 2009. S. 692-704 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5781 LNAI, Nr. PART 1).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
Kloft, M, Nakajima, S
& Brefeld, U 2009,
Feature selection for density level-sets. in W Buntine, M Grobelnik, D Mladenic & J Shawe-Taylor (Hrsg.),
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Nr. PART 1, Bd. 5781 LNAI, Springer, Heidelberg, S. 692-704, European Conference on Machine Learning and Knowledge Discovery in Databases - 2009, Bled, Slowenien,
07.09.09.
https://doi.org/10.1007/978-3-642-04180-8_62APA
Kloft, M., Nakajima, S.
, & Brefeld, U. (2009).
Feature selection for density level-sets. In W. Buntine, M. Grobelnik, D. Mladenic, & J. Shawe-Taylor (Hrsg.),
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (S. 692-704). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5781 LNAI, Nr. PART 1). Springer.
https://doi.org/10.1007/978-3-642-04180-8_62Vancouver
Kloft M, Nakajima S
, Brefeld U.
Feature selection for density level-sets. in Buntine W, Grobelnik M, Mladenic D, Shawe-Taylor J, Hrsg., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Heidelberg: Springer. 2009. S. 692-704. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-04180-8_62
Bibtex
@inbook{fd7170bb236f40429ad4da94cf30074c,
title = "Feature selection for density level-sets",
abstract = "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.",
keywords = "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",
author = "Marius Kloft and Shinichi Nakajima and Ulf Brefeld",
year = "2009",
doi = "10.1007/978-3-642-04180-8_62",
language = "English",
isbn = "978-3-642-04179-2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
number = "PART 1",
pages = "692--704",
editor = "Wray Buntine and Marko Grobelnik and Dunja Mladenic and John Shawe-Taylor",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
note = "European Conference on Machine Learning and Knowledge Discovery in Databases - 2009, ECML-PKDD ; Conference date: 07-09-2009 Through 11-09-2009",
url = "https://www.k4all.org/event/european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery-in-databases/",
}
RIS
TY - CHAP
T1 - Feature selection for density level-sets
AU - Kloft, Marius
AU - Nakajima, Shinichi
AU - Brefeld, Ulf
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Informatics
KW - Concise representations
KW - Cutting plane algorithms
KW - Density levels
KW - Efficient feature selections
KW - Experimental evaluation
KW - Feature selection
KW - Feature sets
KW - Linear programs
KW - Mixing coefficient
KW - Model parameters
KW - Network intrusion detection
KW - Observed data
KW - One-class support vector machine
KW - Semi-infinite
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=70350633038&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04180-8_62
DO - 10.1007/978-3-642-04180-8_62
M3 - Article in conference proceedings
AN - SCOPUS:70350633038
SN - 978-3-642-04179-2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 692
EP - 704
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Buntine, Wray
A2 - Grobelnik, Marko
A2 - Mladenic, Dunja
A2 - Shawe-Taylor, John
PB - Springer
CY - Heidelberg
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases - 2009
Y2 - 7 September 2009 through 11 September 2009
ER -
