Information-theoretic feature selection algorithms for text classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F05%3A00411380" target="_blank" >RIV/67985556:_____/05:00411380 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Information-theoretic feature selection algorithms for text classification
Original language description
Four new algorithms for feature/word selection for the purpose of text classification are presented. Sequential forward selection method based on improved mutual information criterion functions is used. The performance of the proposed criteria compared to the information gain which evaluate features individually is discussed. Experimental results using naive Bayes classifier based on multinomial model, linear support vector machine and k-nearest neighbor classifiers on the Reuters data are analyzed.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the International Joint Conference on Neural Networks
ISBN
0-7803-9048-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE Computational Intelligence Society
Place of publication
Los Alamitos
Event location
Montreal
Event date
Jul 31, 2005
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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