Asymmetric bagging and feature selection for activities prediction of drug molecules
نویسندگان
چکیده
منابع مشابه
Igf-bagging: Information Gain Based Feature Selection for Bagging
Bagging is one of the older, simpler and better known ensemble methods. However, the bootstrap sampling strategy in bagging appears to lead to ensembles of low diversity and accuracy compared with other ensemble methods. In this paper, a new variant of bagging, named IGF-Bagging, is proposed. Firstly, this method obtains bootstrap instances. Then, it employs Information Gain (IG) based feature ...
متن کاملOn Feature Selection, Bias-Variance, and Bagging
We examine the mechanism by which feature selection improves the accuracy of supervised learning. An empirical bias/variance analysis as feature selection progresses indicates that the most accurate feature set corresponds to the best bias-variance trade-off point for the learning algorithm. Often, this is not the point separating relevant from irrelevant features, but where increasing variance...
متن کاملCombining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction
The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect‐prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction res...
متن کاملUnbalance Quantitative Structure Activity Relationship Problem Reduction in Drug Design
Problem statement: Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcome the disadvantage of high cost and long cycle by employing traditional experimental methods. With the fact that number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict molecular activities consi...
متن کاملBagging and Feature Selection for Classification with Incomplete Data
Missing values are an unavoidable issue of many real-world datasets. Dealing with missing values is an essential requirement in classification problem, because inadequate treatment with missing values often leads to large classification errors. Some classifiers can directly work with incomplete data, but they often result in big classification errors and generate complex models. Feature selecti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2008
ISSN: 1471-2105
DOI: 10.1186/1471-2105-9-s6-s7