Please use this identifier to cite or link to this item: https://ir.sc.mahidol.ac.th/handle/123456789/608
Title: PKIP: Feature selection in text categorization for item banks
Authors: Kanlaya Naruedomkul
Keywords: PKIP;lack of words;categorization
Issue Date: 2005
Publisher: ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05
Citation: คณิตศาสตร์
Series/Report no.: ;212-216
Abstract: We propose an alternative approach to text categorization for item banks. An item bank is a collection of textual data in which each item consists of short sentences and has only a few relevant words for categorization; some items could be categorized into many categories. The traditional categorization techniques cannot provide sufficiently accurate results because of a "lack of words" problem. From this observation, items in the same category always have the same group of terms (or keywords) and the similar locations of these terms in phrases suggest that the items have a high probability to be in the same category. Our new methodology PKIP, patterned keywords in phrase, is proposed to improve categorization accuracy and recover from the "lack of words" problem. The k-highest weight order words are selected as the keywords from each category and their patterns are mapped for feature selection. The value of k affects the classification result. The item bank categorization process is based on a supervised machine learning technique. The sample of the item bank that is used in this research is the collection of Thai primary mathematics problems item bank and we use SVM in the Weka machine learning software package as our classifier. The result of the classification shows that our approach produces acceptable classification results and the highest classification result is given when k = 12.
Description: Scopus
URI: https://ir.sc.mahidol.ac.th/handle/123456789/608
ISSN: 10823409
Appears in Collections:Mathematics: International Proceedings

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