Holger Fröhlich and Jörg Kurt Wegner and Andreas Zell

Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression

QSAR Comb. Sci. 2004, 23, pp. 311-318

Abstract

In this paper we present a novel method for selecting descriptor subsets by means of Support Vector Machines in classification and regression - the Incremental Regularized Risk Minimization (IRRM) algorithm. In contrast to many other wrapper methods it is fully deterministic and computationally efficient. We compare our method to existing algorithms and present results on a Human Intestinal Absorption (HIA) classification data set and the Huuskonen regression data set for aqueous solubility.

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Bibtex 

@Article{fwz04,
  author   =     "H. Fr{\"{o}}hlich and J. K. Wegner and A. Zell",
  title    =     "{T}owards {O}ptimal {D}escriptor {S}ubset {S}election with {S}upport {V}ector {M}achines in {C}lassification and {R}egression",
  abstract =     "In this paper we present a novel method for selecting descriptor subsets by means of Support Vector Machines in classification and regression - the Incremental Regularized Risk Minimization (IRRM) algorithm. In contrast to many other wrapper methods it is fully deterministic and computationally efficient. We compare our method to existing algorithms and present results on a Human Intestinal Absorption (HIA) classification data set and the Huuskonen regression data set for aqueous solubility.",
  journal  =     "QSAR Comb. Sci.",
  volume   =     "23",
  year     =     "2004",
  pages    =     "311--318",
  url      =     "http://dx.doi.org/10.1002/qsar.200410011",
  doi      =     "10.1002/qsar.200410011",
  note     =     "",
  contents =     "Descriptor Selection, Support Vector Machinesm, Human Intestinal Absorption, aqueous solubility",
  topics =       "Descriptor Selection, Support Vector Machinesm, Human Intestinal Absorption, aqueous solubility",
}