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
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;
Conference_Titel :
Automation and Logistics (ICAL), 2011 IEEE International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-0301-0
Electronic_ISBN :
2161-8151
DOI :
10.1109/ICAL.2011.6024771