NDPMine: Efficiently mining discriminative numerical features for pattern-based classification

Hyungsul Kim, Sangkyum Kim, Tim Weninger, Jiawei Han, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
Pages35-50
Number of pages16
EditionPART 2
DOIs
StatePublished - 2010
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spain
Duration: Sep 20 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6322 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
CountrySpain
CityBarcelona
Period9/20/109/24/10

Keywords

  • Discriminative Pattern Mining
  • Pattern-Based Classification
  • SVM

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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