Automated Approaches for Classifying Structures

Abstract

In this paper we study the problem of classifying chemical compound datasets. We present an algorithm that first mines the chemical compound dataset to discover discriminating sub-structures; these discriminating sub-structures are used as features to build a powerful classifier. The advantage of our classification technique is that it requires very little domain knowledge and can easily handle large chemical datasets. We evaluated the performance of our classifier on two widely available chemical compound datasets and have found it to give good results.

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Document Details

Document Type
Technical Report
Publication Date
Jun 26, 2002
Accession Number
ADA439498

Entities

People

  • George Karypis
  • Michihiro Kuramochi
  • Mukund Deshpande

Organizations

  • University of Minnesota

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Bioassay
  • Chemical Compounds
  • Classification
  • Computational Complexity
  • Computer Science
  • Engineering
  • Feature Selection
  • Information Operations
  • Machine Learning
  • Military Research
  • New York
  • Supervised Machine Learning
  • Test And Evaluation
  • Toxicology

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Surface Engineering/Surface Coating Technology.