Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2003

Abstract

This paper reports our comparative evaluation of three machine learning methods, namely k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) for Chinese document categorization. Based on two Chinese corpora, a series of controlled experiments evaluated their learning capabilities and efficiency in mining text classification knowledge. Benchmark experiments showed that their predictive performance were roughly comparable, especially on clean and well organized data sets. While kNN and ARAM yield better performances than SVM on small and clean data sets, SVM and ARAM significantly outperformed kNN on noisy data. Comparing efficiency, kNN was notably more costly in terms of time and memory than the other two methods. SVM is highly efficient in learning from well organized samples of moderate size, although on relatively large and noisy data the efficiency of SVM and ARAM are comparable.

Keywords

text categorization, machine learning, comparative experiments

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Applied Intelligence

Volume

18

Issue

3

First Page

311

Last Page

322

ISSN

0924-669X

Identifier

10.1023%2FA%3A1023202221875

Publisher

Springer (part of Springer Nature): Springer Open Choice Hybrid Journals

Additional URL

https://doi.org/10.1023%2FA%3A1023202221875

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