Learning Neural Network Ensemble for Practical Text Classification

Sung Bae Cho, Jee Haeng Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Automated text classification has been considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been applied by machine learning technologies such as k-nearest neighbor, decision tree, support vector machine, and neural networks. However, most of the investigations in text classification are studied not on real data but on well-organized text corpus, and do not show their usefulness. This paper suggests and analyzes text classification method for a real application, FAQ text classification task, by combining multiple classifiers. We propose two methods of combining multiple neural networks that improve performance by maximum combining and neural network combining. Experimental results show the usefulness of proposed methods for real application domain.

Original languageEnglish
Pages (from-to)1032-1036
Number of pages5
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2690
Publication statusPublished - 2004 Dec 1

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Neural Network Ensemble
Text Classification
Neural networks
Neural Networks
Multiple Classifiers
Decision trees
Decision tree
Support vector machines
Learning systems
Nearest Neighbor
Support Vector Machine
Machine Learning
Classifiers
Learning
Experimental Results

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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