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 -