Image Classification using Hybrid Data Mining Algorithms - A Review

被引：0
|
作者：
Thamilselvan, P. [1 ]
Sathiaseelan, J. G. R. [1 ]
机构：
[1] Bishop Heber Coll, Dept Comp Sci, Tiruchirappalli, Tamil Nadu, India
来源：
2015 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS) | 2015年
关键词：
Hybrid Approach; Image Classification; Image Mining; Image datasets; Mining algorithms; AUTOMATED DETECTION; RETINAL IMAGES; SVM;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Data mining is one of the most significant research area in computer science. It is a calculation process of finding and determining valuable information from huge data set. Image classification is an important technique to generate valuable information. The classification method provides the accurate result in their target class. This review compares the some predominant hybrid classification algorithms to find the classification accuracy for various data sets and their performance of techniques. It provides some important hybrid techniques that have been used for image classification. In this paper the hybrid data mining algorithms are studied like GA-SVM, EKM-EELM, AdaBoost-SVM, Decision Tree-Naive Bayes, and SVM-CART.
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页数：6
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