کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970157 | 1450030 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A probabilistic multi-label classifier with missing and noisy labels handling capability
ترجمه فارسی عنوان
یک طبقه بندی احتمالی چند لایک با برچسب های گم شده و پر سر و صدا دست زدن به قابلیت
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Multi-label classification with a large set of labels is a challenging task. Label-Space Dimension Reduction (LSDR) is the most popular approach that addresses this problem. LSDR methods project the high-dimensional label vectors onto a low-dimensional space that can be predicted from the feature space. Many LSDR methods assume that the training data provide complete label vector for all training samples while this assumption is usually violated particularly when label vectors are high dimensional. In this paper, we propose a probabilistic model that has an effective mechanism to handle missing and noisy labels. In the proposed Bayesian network model, a set of auxiliary random variables, called experts, are incorporated to provide robustness to missing and noisy labels. Variational inference is utilized to find the desired probabilities in this model. The proposed approximate inference is highly parallelizable and can be implemented efficiently. Experiments on real-world datasets show that our method outperforms state-of-the-art multi-label classifiers by a large margin.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 89, 1 April 2017, Pages 18-24
Journal: Pattern Recognition Letters - Volume 89, 1 April 2017, Pages 18-24
نویسندگان
Amirhossein Akbarnejad, Mahdieh Soleymani Baghshah,