DocumentCode
568072
Title
A new minority kind of sample sampling method based on genetic algorithm and K-means cluster
Author
Yong, Yang ; Gao Xin-cheng
Author_Institution
Sch. of Comput. & Inf. Technol., Northeast Pet. Univ., Daqing, China
fYear
2012
fDate
14-17 July 2012
Firstpage
126
Lastpage
129
Abstract
In view of the classification favors seriously to the most kinds when we use the traditional sorter to classify the imbalanced data set and the errors of classification of minority kind is big, A new minority kind of sample sampling method based on genetic algorithm and K-means cluster is proposed. First the method clusters and groups the minority kind of sample through K-means algorithm, then gains the new sample in each cluster through the genetic algorithm and the valid confirmation is proceed. Finally, The validity of experimental results is proved through using SVM and KNN sorter.
Keywords
genetic algorithms; pattern classification; pattern clustering; sampling methods; support vector machines; K-means algorithm; K-means cluster; KNN sorter; SVM sorter; classification errors; genetic algorithm; imbalanced data set classification; minority kind sample sampling method; Accuracy; Biological cells; Classification algorithms; Clustering algorithms; Educational institutions; Genetic algorithms; Support vector machines; Cluster; Genetic algorithm; Imbalanced data set; K-means algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location
Melbourne, VIC
Print_ISBN
978-1-4673-0241-8
Type
conf
DOI
10.1109/ICCSE.2012.6295041
Filename
6295041
Link To Document