Improve Abstract Data with Feature Selection for Classification Techniques

被引：0
|
作者：
Nuipian, Vatinee [1 ,2 ]
Meesad, Phayung [3 ]
Boonrawd, Pudsadee [2 ]
机构：
[1] King Mongkuts Univ Technol North Bangkok, Inst Comp & Informat Technol, Bangkok, Thailand
[2] King Mongkuts Univ Technol North Bangkok, Fac Informat Technol, Dept Informat Technol, Bangkok, Thailand
[3] King Mongkuts Univ Technol North Bangkok, Fac Tech Educ, Dept Teacher Training Elect Engn, Bangkok, Thailand
来源：
FUTURE INFORMATION TECHNOLOGY | 2011年 / 13卷
关键词：
digital library; text classification; feature selection; support vector machine; SVM Attribute;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A universal problem with text classification has a problem due to the high dimensionality of feature space, e.g. word frequency vectors. To overcome this problem, this paper proposed a feature selection which focuses on statistical pattern based on SVM Attribute. Experiments have shown that the determination of word importance may increase the speed of the classification algorithm and save their resource used significantly. The proposed method was studied by comparing classification performance among Decision Tree, Naive Bayes, and Support Vector Machine. The results showed that Support Vector Machine was found to be the best algorithm with F-measure 93.6%. It is found that the feature selection can reduce dimensionality of data significantly.
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页码：213 / 217
页数：5
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