کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939256 1449970 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Feature selection method with joint maximal information entropy between features and class
ترجمه فارسی عنوان
روش انتخاب ویژگی با انتگرال اطلاعات حداکثر مشترک بین ویژگی ها و کلاس
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Feature selection remains a popular method for quantity reduction of attributes of high-dimensional data, to reduce computational costs in classifications. A new feature selection method based on the joint maximal information entropy between features and class (FS-JMIE) is proposed in this paper. Firstly, the joint maximal information entropy (JMIE) is defined to measure a feature subset. Next, a binary particle swarm optimization (BPSO) algorithm is introduced to search the optimal feature subset. Finally, classification is performed on UCI corpora to verify the performance of our proposed method compared to the traditional mutual information (MI) method, CHI method, as well as a binary version of particle swarm optimization-support vector machines (BPSO-SVMs) feature selection. Experiments show that FS-JMIE achieves an equal or better performance than MI, CHI, and BPSO-SVM. Further, FS-JMIE manifests relatively better robustness to the number of classes. Moreover, the method shows higher consistency and better time-efficiency than BPSO-SVM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 77, May 2018, Pages 20-29
نویسندگان
, ,