Examining text categorization methods for incidents analysis
Text mining saves the necessity to sift through vast amount of documents manually to find relevant information. This paper focuses on text categorization, one of the tasks under text mining. This paper introduces fuzzy grammar as a technique for building text classifier and investigates the performa...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Section |
| Published: |
Springer
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/26093/ |
| _version_ | 1848845486807580672 |
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| author | Mohd Sharef, Nurfadhlina Kasmiran, Khairul Azhar |
| author2 | Chau, Michael |
| author_facet | Chau, Michael Mohd Sharef, Nurfadhlina Kasmiran, Khairul Azhar |
| author_sort | Mohd Sharef, Nurfadhlina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Text mining saves the necessity to sift through vast amount of documents manually to find relevant information. This paper focuses on text categorization, one of the tasks under text mining. This paper introduces fuzzy grammar as a technique for building text classifier and investigates the performance of fuzzy grammar against other machine learning methods such as decision table, support vector machine, statistic, nearest neighbor and boosting. Incidents data set was used where the focus was given on classifying the incidents events. Results have shown that fuzzy grammar has gotten promising results among the other benchmark machine learning methods. |
| first_indexed | 2025-11-15T08:47:35Z |
| format | Book Section |
| id | upm-26093 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T08:47:35Z |
| publishDate | 2012 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-260932016-01-19T04:15:12Z http://psasir.upm.edu.my/id/eprint/26093/ Examining text categorization methods for incidents analysis Mohd Sharef, Nurfadhlina Kasmiran, Khairul Azhar Text mining saves the necessity to sift through vast amount of documents manually to find relevant information. This paper focuses on text categorization, one of the tasks under text mining. This paper introduces fuzzy grammar as a technique for building text classifier and investigates the performance of fuzzy grammar against other machine learning methods such as decision table, support vector machine, statistic, nearest neighbor and boosting. Incidents data set was used where the focus was given on classifying the incidents events. Results have shown that fuzzy grammar has gotten promising results among the other benchmark machine learning methods. Springer Chau, Michael Wang, G. Alan Wei, Thoo Yue Chen, Hsinchun 2012 Book Section PeerReviewed Mohd Sharef, Nurfadhlina and Kasmiran, Khairul Azhar (2012) Examining text categorization methods for incidents analysis. In: Intelligence and Security Informatics. Lecture Notes in Computer Science . Springer, Berlin, pp. 154-161. ISBN 9783642304279 10.1007/978-3-642-30428-6_13 |
| spellingShingle | Mohd Sharef, Nurfadhlina Kasmiran, Khairul Azhar Examining text categorization methods for incidents analysis |
| title | Examining text categorization methods for incidents analysis |
| title_full | Examining text categorization methods for incidents analysis |
| title_fullStr | Examining text categorization methods for incidents analysis |
| title_full_unstemmed | Examining text categorization methods for incidents analysis |
| title_short | Examining text categorization methods for incidents analysis |
| title_sort | examining text categorization methods for incidents analysis |
| url | http://psasir.upm.edu.my/id/eprint/26093/ http://psasir.upm.edu.my/id/eprint/26093/ |