کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
530682 | 869782 | 2014 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Enhancing multi-label classification by modeling dependencies among labels
ترجمه فارسی عنوان
افزایش طبقه بندی چند لایحه با مدل سازی وابستگی ها در میان برچسب ها
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کلمات کلیدی
طبقه بندی چند لایک، شبکه بیزی، ساختار یادگیری، انتساب برچسب ناقص، برآورد حداکثر احتمال،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we propose a novel framework for multi-label classification, which directly models the dependencies among labels using a Bayesian network. Each node of the Bayesian network represents a label, and the links and conditional probabilities capture the probabilistic dependencies among multiple labels. We employ our Bayesian network structure learning method, which guarantees to find the global optimum structure, independent of the initial structure. After structure learning, maximum likelihood estimation is used to learn the conditional probabilities among nodes. Any current multi-label classifier can be employed to obtain the measurements of labels. Then, using the learned Bayesian network, the true labels are inferred by combining the relationship among labels with the labels׳ estimates obtained from a current multi-labeling method. We further extend the proposed multi-label classification method to deal with incomplete label assignments. Structural Expectation-Maximization algorithm is adopted for both structure and parameter learning. Experimental results on two benchmark multi-label databases show that our approach can effectively capture the co-occurrent and the mutual exclusive relation among labels. The relation modeled by our approach is more flexible than the pairwise or fixed subset labels captured by current multi-label learning methods. Thus, our approach improves the performance over current multi-label classifiers. Furthermore, our approach demonstrates its robustness to incomplete multi-label classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 47, Issue 10, October 2014, Pages 3405-3413
Journal: Pattern Recognition - Volume 47, Issue 10, October 2014, Pages 3405-3413
نویسندگان
Shangfei Wang, Jun Wang, Zhaoyu Wang, Qiang Ji,