کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939191 1449969 2018 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel random forests based class incremental learning method for activity recognition
ترجمه فارسی عنوان
یک روش یادگیری پیشرفته کلاس برای شناسایی فعالیت یک جنگل تصادفی جدید
کلمات کلیدی
یادگیری کلاس افزایشی، به رسمیت شناختن فعالیت جنگل های تصادفی، 00-01، 99-00،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Automatic activity recognition is an active research topic which aims to identify human activities automatically. A significant challenge is to recognize new activities effectively. In this paper, we propose an effective class incremental learning method, named Class Incremental Random Forests (CIRF), to enable existing activity recognition models to identify new activities. We design a separating axis theorem based splitting strategy to insert internal nodes and adopt Gini index or information gain to split leaves of the decision tree in the random forests (RF). With these two strategies, both inserting new nodes and splitting leaves are allowed in the incremental learning phase. We evaluate our method on three UCI public activity datasets and compare with other state-of-the-art methods. Experimental results show that the proposed incremental learning method converges to the performance of batch learning methods (RF and extremely randomized trees). Compared with other state-of-the-art methods, it is able to recognize new class data continuously with a better performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 78, June 2018, Pages 277-290
نویسندگان
, , , ,