Improvement of Classification for Agriculture Bibliographic Data
Article Sidebar
Main Article Content
Abstract
In general, Classifying bibliographic data using lexicon-based might not enough in terms of classification efficiency. In this paper, we propose the agriculture bibliographic classification model by improving lexicon set and by using feature selection techniques. The techniques of Information Gain (IG), Chi Squared (CHI) and Gain Ratio (GR) are used in order to select the distinguish properties for feature selection process. Then three algorithms Decision Tree (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) are applied to classify those features. The experiments were done by using 2,580 papers from agriculture publication database. The results show that the proposed method gave better performance than using only lexicon-based in terms of precision/recall and F-measure, respectively 1.3%.
การเพิ่มประสิทธิภาพการจำแนกหมวดหมู่ข้อมูลเชิงบรรณานุกรมด้านการเกษตร
บทคัดย่อไม่สมบูรณ์
Article Details
It is the policy of ACTISNU to own the copyright to the published contributions on behalf of the interests of ACTISNU, its authors, and their employers, and to facilitate the appropriate reuse of this material by others. To comply with the Copyright Law, authors are required to sign an ACTISNU copyright transfer form before publication. This form, a copy of which appears in this journal (or website), returns to authors and their employers full rights to reuse their material for their own purposes.