Predictive self-organizing networks for text categorization

Predictive self-organizing networks for text categorization

This paper introduces a class of predictive self-organizing neural networks known as Adaptive Resonance Associative Map (ARAM) for classification of free-text documents. Whereas most sta- tistical approaches to text categorization derive classification knowledge based on training examples alone, ARA...

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Bibliographic Details
Main Author: TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2001
Subjects:
Data mining
Information retrieval systems
Knowledge based systems
Neural networks
Databases and Information Systems
Numerical Analysis and Scientific Computing
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/6280
https://ink.library.smu.edu.sg/context/sis_research/article/7283/viewcontent/tc_pakdd01.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper introduces a class of predictive self-organizing neural networks known as Adaptive Resonance Associative Map (ARAM) for classification of free-text documents. Whereas most sta- tistical approaches to text categorization derive classification knowledge based on training examples alone, ARAM performs supervised learn- ing and integrates user-defined classification knowledge in the form of IF-THEN rules. Through our experiments on the Reuters-21578 news database, we showed that ARAM performed reasonably well in mining categorization knowledge from sparse and high dimensional document feature space. In addition, ARAM predictive accuracy and learning efficiency can be improved by incorporating a set of rules derived from the Reuters category description. The impact of rule insertion is most significant for categories with a small number of relevant documents.

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