Feature Selection Algorithm for Hyperlipidemia Classification

Abstract:

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ² statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averaging F₁ measure is used. DF is suitable for the task of large text classification.

• Periodical: Applied Mechanics and Materials (Volumes 701-702)
• Pages: 110-113
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.701-702.110
• Online since: December 2014
• Authors: Qi Rui Zhang*, He Xian Wang, Jiang Wei Qin
• Keywords: Feature Selection, Hyperlipemia, Text Categorization

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