DocumentCode
3382786
Title
Functional gene prediction with vital reduced features: Further topics for feature reduction and evaluation criteria for classifiers
Author
Xiaochuan Ai ; Jingbo Xia
Author_Institution
Coll. of Sci., Naval Univ. of Eng., Wuhan, China
fYear
2011
fDate
15-16 Aug. 2011
Firstpage
501
Lastpage
506
Abstract
Aiming at the prediction of protein solubility, four feature reduction methods are discussed in this paper, including Correlation coefficient method, Filter method, Relief method and Genetic method. With the Top 100 features discovered by genetic method, the best classifier achieves the accuracy of 86% and MCC of 0.7236 in Jackknife test. Moreover, further discussions about feature reduction and classifier reliability evaluation criteria are given. The author claim the exclusive importance of capacity of expansion prediction for classifiers.
Keywords
biology computing; data mining; pattern classification; support vector machines; Jackknife test; SVM; correlation coefficient method; data mining; evaluation classifiers; evaluation criteria; feature reduction methods; filter method; functional gene prediction; genetic method; protein solubility; relief method; support vector machine; vital reduced features; Accuracy; Correlation; Feature extraction; Genetics; Proteins; Reliability; Support vector machines; Support vector machine; classification; cross validation; reliability; verification;
fLanguage
English
Publisher
ieee
Conference_Titel
Automation and Logistics (ICAL), 2011 IEEE International Conference on
Conference_Location
Chongqing
ISSN
2161-8151
Print_ISBN
978-1-4577-0301-0
Electronic_ISBN
2161-8151
Type
conf
DOI
10.1109/ICAL.2011.6024771
Filename
6024771
Link To Document