کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969517 1449977 2017 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hierarchical Multi-label Classification using Fully Associative Ensemble Learning
ترجمه فارسی عنوان
طبقه بندی چند لایحه سلسله مراتبی با استفاده از آموزش کامل گروهی وابسته
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Traditional flat classification methods (e.g., binary or multi-class classification) neglect the structural information between different classes. In contrast, Hierarchical Multi-label Classification (HMC) considers the structural information embedded in the class hierarchy, and uses it to improve classification performance. In this paper, we propose a local hierarchical ensemble framework for HMC, Fully Associative Ensemble Learning (FAEL). We model the relationship between each class node's global prediction and the local predictions of all the class nodes as a multi-variable regression problem with Frobenius norm or l1 norm regularization. It can be extended using the kernel trick, which explores the complex correlation between global and local prediction. In addition, we introduce a binary constraint model to restrict the optimal weight matrix learning. The proposed models have been applied to image annotation and gene function prediction datasets with tree structured class hierarchy and large scale visual recognition dataset with Direct Acyclic Graph (DAG) structured class hierarchy. The experimental results indicate that our models achieve better performance when compared with other baseline methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 70, October 2017, Pages 89-103
نویسندگان
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