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Examining text categorization methods for incidents analysis


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


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.

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Additional Metadata

Item Type: Book Section
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1007/978-3-642-30428-6_13
Publisher: Springer
Keywords: Text categorization; Text mining; Incidents classification; Fuzzy grammar; Machine learning
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 19 Jan 2016 04:15
Last Modified: 19 Jan 2016 04:15
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/978-3-642-30428-6_13
URI: http://psasir.upm.edu.my/id/eprint/26093
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