Adaptive sparse grid classification using grid environments

Dirk Pflüger, Ioan Lucian Muntean, Hans Joachim Bungartz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used. This allows for classification algorithms scaling only linearly in the number of training data points, enabling to learn from data sets with millions of data points. As the curse of dimensionality prohibits the use of standard grids, sparse grids have to be used. Adaptive sparse grids allow to get a trade-off between both worlds by refining in rough regions of the target function rather than in smooth ones. We present new results for some typical classification tasks and show first observations of dimension adaptivity. As the study of the critical parameters during development involves many computations for different parameter values, we used a grid environment which we present.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings, Part I
PublisherSpringer Verlag
Pages708-715
Number of pages8
ISBN (Print)9783540725831
DOIs
StatePublished - 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: 27 May 200730 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4487 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Science, ICCS 2007
Country/TerritoryChina
CityBeijing
Period27/05/0730/05/07

Keywords

  • Adaptive sparse grids
  • Classification
  • Data mining
  • Grid environment

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