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
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;
Conference_Titel :
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4673-0241-8
DOI :
10.1109/ICCSE.2012.6295041