Title :
A new method for classification in DNA sequence
Author :
Qingda Zhou ; Qingshan Jiang ; Dan Wei
Author_Institution :
Software Sch., Univ. of Xiamen, Xiamen, China
Abstract :
As an important part of biological sequence data, DNA sequence determines the type and function of the DNA. Wiping off the independent random background in the process of DNA sequence feature extraction to solve the repeated computation in information extraction, then using the k-means method to cluster the dataset, and SVM algorithm in classification respectively, the method in this paper finally determines the final result according to voting, and experiment results show that the algorithm has better search efficiency, and can get better research results.
Keywords :
bioinformatics; feature extraction; pattern classification; support vector machines; DNA sequence classification; DNA sequence feature extraction; SVM algorithm; bioinformatics; biological sequence data; independent random background; information extraction; k-means method; support vector machine; Algorithm design and analysis; Artificial neural networks; Classification algorithms; DNA; Feature extraction; Microorganisms; Support vector machines; DNA sequence; classification; data partition; feature extraction;
Conference_Titel :
Computer Science & Education (ICCSE), 2011 6th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-9717-1
DOI :
10.1109/ICCSE.2011.6028621
