Poem Classification Using Machine Learning Approach

被引：3
|
作者：
Kumar, Vipin [1 ]
Minz, Sonajharia [1 ]
机构：
[1] JNU, New Delhi, India
来源：
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012) | 2014年 / 236卷
关键词：
Poem; Classification; Ranked feature;
D O I：
10.1007/978-81-322-1602-5_72
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The collection of poems is ever increasing on the Internet. Therefore, classification of poems is an important task along with their labels. The work in this paper is aimed to find the best classification algorithms among the K-nearest neighbor (KNN), Naive Bayesian (NB) and Support Vector Machine (SVM) with reduced features. Information Gain Ratio is used for feature selection. The results show that SVM has maximum accuracy (93.25%) using 20% top ranked features.
引用
收藏
页码：675 / 682
页数：8
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