Abstract
In a learning process, features play a fundamental role. In this paper, we propose a Boosting-based feature selection algorithm called BoostFS. It extends AdaBoost which is designed for classification problems to feature selection. BoostFS maintains a distribution over training samples which is initialized from the uniform distribution. In each iteration, a decision stump is trained under the sample distribution and then the sample distribution is adjusted so that it is orthogonal to the classification results of all the generated stumps. Because a decision stump can also be regarded as one selected feature, BoostFS is capable to select a subset of features that are irrelevant to each other as much as possible. Experimental results on synthetic datasets, five UCI datasets and a real malware detection dataset all show that the features selected by BoostFS help to improve learning algorithms in classification problems, especially when the original feature set contains redundant features.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Pattern Recognition and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.