Comparison of SVM and some older classification algorithms in text classification tasks

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, vario...

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Autores principales: Colas, Fabrice, Brazdil, Pavel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
gestión de documentos
support vector machine
Model classification
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23885
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23885
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
gestión de documentos
support vector machine
Model classification
Algorithms
spellingShingle Ciencias Informáticas
gestión de documentos
support vector machine
Model classification
Algorithms
Colas, Fabrice
Brazdil, Pavel
Comparison of SVM and some older classification algorithms in text classification tasks
topic_facet Ciencias Informáticas
gestión de documentos
support vector machine
Model classification
Algorithms
description Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM We have decided to investigate this issue and compared SVM to kNN and naive Bayes on binary classification tasks. An important issue is to compare optimized versions of these algorithms, which is what we have done. Our results show all the classifiers achieved comparable performance on most problems. One surprising result is that SVM was not a clear winner, despite quite good overall performance. If a suitable preprocessing is used with kNN, this algorithm continues to achieve very good results and scales up well with the number of documents, which is not the case for SVM. As for naive Bayes, it also achieved good performance.
format Objeto de conferencia
Objeto de conferencia
author Colas, Fabrice
Brazdil, Pavel
author_facet Colas, Fabrice
Brazdil, Pavel
author_sort Colas, Fabrice
title Comparison of SVM and some older classification algorithms in text classification tasks
title_short Comparison of SVM and some older classification algorithms in text classification tasks
title_full Comparison of SVM and some older classification algorithms in text classification tasks
title_fullStr Comparison of SVM and some older classification algorithms in text classification tasks
title_full_unstemmed Comparison of SVM and some older classification algorithms in text classification tasks
title_sort comparison of svm and some older classification algorithms in text classification tasks
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23885
work_keys_str_mv AT colasfabrice comparisonofsvmandsomeolderclassificationalgorithmsintextclassificationtasks
AT brazdilpavel comparisonofsvmandsomeolderclassificationalgorithmsintextclassificationtasks
bdutipo_str Repositorios
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