caa a22 4500 605468605 CHVBK 20210128100317.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1358-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1358-x A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization [Elektronische Daten] [Nurfadhlina Sharef, Trevor Martin, Khairul Kasmiran, Aida Mustapha, Md. Sulaiman, Masrah Azmi-Murad] Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Naïve Bayes, support vector machine, $$k$$ k -nearest neighbor, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while ImageCLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both categorization tasks, where a standard evaluation technique (i.e., recall, precision, and $$F$$ F -measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task. Springer-Verlag Berlin Heidelberg, 2014 Text categorization nationallicence Text expression nationallicence Evolving fuzzy grammar nationallicence Machine learning nationallicence Incidents nationallicence Medical nationallicence Sharef Nurfadhlina Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Martin Trevor Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Kasmiran Khairul Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Mustapha Aida Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Sulaiman Md Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Azmi-Murad Masrah Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1701-1714 1432-7643 19:6<1701 2015 19 500 https://doi.org/10.1007/s00500-014-1358-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1358-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sharef Nurfadhlina Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 700 1- Martin Trevor Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 700 1- Kasmiran Khairul Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 700 1- Mustapha Aida Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 700 1- Sulaiman Md Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 700 1- Azmi-Murad Masrah Faculty of Computer Science and Information Technology, University of Putra Malaysia, 43400, Serdang, Selangor, Malaysia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1701-1714 1432-7643 19:6<1701 2015 19 500