Medical text categorization using SEBLA and Kernel Discriminant Analysis
作者： Muhammad Atif Tahir , Emdad Khan , Adel Al Salem
DOI: 10.1109/WSWAN.2015.7210310
关键词: Text categorization 、 Tree kernel 、 Component (UML) 、 Natural language processing 、 Computer science 、 Machine learning 、 Categorization 、 Field (computer science) 、 Text mining 、 Educational data 、 Artificial intelligence 、 Kernel Fisher discriminant analysis
摘要: Education data mining (EDM) is an emerging field which take advantage of natural language processing, mining, statistics and machine learning algorithms for different types educational especially related to medical e-learning. Medical text categorization the one major component help students more easily effectively search e-Learning. In this paper, spectral based Kernel Discriminant Analysis has been introduced categorization. We evaluated proposed approach on 10 most frequent categories cardiovascular diseases group from Ohsumed sets. When compared with existing approaches, results have indicated significant increase in performance. order further refine search, Semantic Engine that uses Brain-Like (SEBLA) also paper.
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