کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6861772 | 1439258 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Online Multi-label Group Feature Selection
ترجمه فارسی عنوان
انتخاب چندین برچسب گروه آنلاین
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کلمات کلیدی
انتخاب آنلاین، یادگیری چند برچسب، ویژگی جریان انتخاب گروهی گروه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Feature selection for multi-label learning has received intensive interest in recent years. However, traditional multi-label feature selection are incapable of considering intrinsic group structures of features and handling streaming features simultaneously. To solve this problem, we develop an algorithm called Online Multi-label Group Feature Selection (OMGFS). Our proposed method consists of two-phase: online group selection and online inter-group selection. In the group selection, we design a criterion to select feature groups which is important to label set. In the inter-group selection, we consider feature interaction and feature redundancy to select an optimal feature subset. This two-phase procedure continues until there are no more features arriving. An empirical study using a series of benchmark data sets demonstrates that the proposed method outperforms other state-of-the-art multi-label feature selection methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 143, 1 March 2018, Pages 42-57
Journal: Knowledge-Based Systems - Volume 143, 1 March 2018, Pages 42-57
نویسندگان
Jinghua Liu, Yaojin Lin, Shunxiang Wu, Chenxi Wang,