One-class document classification via Neural Networks

Larry Manevitz, Malik Yousef

Research output: Contribution to journalArticlepeer-review

166 Scopus citations

Abstract

Automated document retrieval and classification is of central importance in many contexts; our main motivating goal is the efficient classification and retrieval of "interests" on the internet when only positive information is available. In this paper, we show how a simple feed-forward neural network can be trained to filter documents under these conditions, and that this method seems to be superior to modified methods (modified to use only positive examples), such as Rocchio, Nearest Neighbor, Naive-Bayes, Distance-based Probability and One-Class SVM algorithms. A novel experimental finding is that retrieval is enhanced substantially in this context by carrying out a certain kind of uniform transformation ("Hadamard") of the information prior to the training of the network.

Original languageEnglish
Pages (from-to)1466-1481
Number of pages16
JournalNeurocomputing
Volume70
Issue number7-9
DOIs
StatePublished - Mar 2007
Externally publishedYes

Keywords

  • Autoencoder
  • Automated document retrieval
  • Bottleneck neural network
  • Classification
  • Feed-forward neural networks
  • Machine learning
  • One-class classification

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