Benchmarking the performance of support vector machines in classifying web pages

Wein Pei Wong; Ke Xin Chan; Lay Ki Soon

doi:10.1007/978-3-642-32826-8_42

Benchmarking the performance of support vector machines in classifying web pages

Wein Pei Wong, Ke Xin Chan, Lay Ki Soon

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

In this paper, we benchmark the efficiency of support vector machines (SVMs), in terms of classification accuracy and the classification speed with the other two popular classification algorithms, which are decision tree and Naïve Bayes. We conduct the study on the 4-University data set, using 4-fold cross validation. The empirical results indicate that both SVMs and Naïve Bayes achieve comparative results in the average precision and recall while decision tree ID3 algorithm outperforms the rest in the average accuracy despite. Nevertheless, ID3 consumes the longest time in generating the classification model as well as classifying the web pages.

Original language	English
Title of host publication	Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers
Pages	375-378
Number of pages	4
DOIs	https://doi.org/10.1007/978-3-642-32826-8_42
Publication status	Published - 2012
Externally published	Yes
Event	Knowledge Technology Week 2011 - Kajang, Malaysia Duration: 18 Jul 2011 → 22 Jul 2011 Conference number: 3rd https://link.springer.com/book/10.1007%2F978-3-642-32826-8 (SpringerLink - entire proceedings)

Publication series

Name	Communications in Computer and Information Science
Volume	295 CCIS
ISSN (Print)	1865-0929

Conference

Conference	Knowledge Technology Week 2011
Abbreviated title	KTW 2011
Country/Territory	Malaysia
City	Kajang
Period	18/07/11 → 22/07/11
Internet address	https://link.springer.com/book/10.1007%2F978-3-642-32826-8 (SpringerLink - entire proceedings)

Keywords

decision tree
Naïve Bayes
support vector machines
web classification

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10.1007/978-3-642-32826-8_42

Cite this

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title = "Benchmarking the performance of support vector machines in classifying web pages",

abstract = "In this paper, we benchmark the efficiency of support vector machines (SVMs), in terms of classification accuracy and the classification speed with the other two popular classification algorithms, which are decision tree and Na{\"i}ve Bayes. We conduct the study on the 4-University data set, using 4-fold cross validation. The empirical results indicate that both SVMs and Na{\"i}ve Bayes achieve comparative results in the average precision and recall while decision tree ID3 algorithm outperforms the rest in the average accuracy despite. Nevertheless, ID3 consumes the longest time in generating the classification model as well as classifying the web pages.",

keywords = "decision tree, Na{\"i}ve Bayes, support vector machines, web classification",

author = "Wong, {Wein Pei} and Chan, {Ke Xin} and Soon, {Lay Ki}",

year = "2012",

doi = "10.1007/978-3-642-32826-8_42",

language = "English",

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series = "Communications in Computer and Information Science",

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}

Wong, WP, Chan, KX & Soon, LK 2012, Benchmarking the performance of support vector machines in classifying web pages. in Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. Communications in Computer and Information Science, vol. 295 CCIS, pp. 375-378, Knowledge Technology Week 2011, Kajang, Malaysia, 18/07/11. https://doi.org/10.1007/978-3-642-32826-8_42

Benchmarking the performance of support vector machines in classifying web pages. / Wong, Wein Pei; Chan, Ke Xin; Soon, Lay Ki.
Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers. 2012. p. 375-378 (Communications in Computer and Information Science; Vol. 295 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Benchmarking the performance of support vector machines in classifying web pages

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AU - Chan, Ke Xin

AU - Soon, Lay Ki

N1 - Conference code: 3rd

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N2 - In this paper, we benchmark the efficiency of support vector machines (SVMs), in terms of classification accuracy and the classification speed with the other two popular classification algorithms, which are decision tree and Naïve Bayes. We conduct the study on the 4-University data set, using 4-fold cross validation. The empirical results indicate that both SVMs and Naïve Bayes achieve comparative results in the average precision and recall while decision tree ID3 algorithm outperforms the rest in the average accuracy despite. Nevertheless, ID3 consumes the longest time in generating the classification model as well as classifying the web pages.

AB - In this paper, we benchmark the efficiency of support vector machines (SVMs), in terms of classification accuracy and the classification speed with the other two popular classification algorithms, which are decision tree and Naïve Bayes. We conduct the study on the 4-University data set, using 4-fold cross validation. The empirical results indicate that both SVMs and Naïve Bayes achieve comparative results in the average precision and recall while decision tree ID3 algorithm outperforms the rest in the average accuracy despite. Nevertheless, ID3 consumes the longest time in generating the classification model as well as classifying the web pages.

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KW - Naïve Bayes

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KW - web classification

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DO - 10.1007/978-3-642-32826-8_42

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SN - 9783642328251

T3 - Communications in Computer and Information Science

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BT - Knowledge Technology - Third Knowledge Technology Week, KTW 2011, Revised Selected Papers

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Y2 - 18 July 2011 through 22 July 2011

ER -

Benchmarking the performance of support vector machines in classifying web pages

Abstract

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Conference

Keywords

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