کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406199 678069 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A robust ensemble approach to learn from positive and unlabeled data using SVM base models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A robust ensemble approach to learn from positive and unlabeled data using SVM base models
چکیده انگلیسی

We present a novel approach to learn binary classifiers when only positive and unlabeled instances are available (PU learning). This problem is routinely cast as a supervised task with label noise in the negative set. We use an ensemble of SVM models trained on bootstrap resamples of the training data for increased robustness against label noise. The approach can be considered in a bagging framework which provides an intuitive explanation for its mechanics in a semi-supervised setting. We compared our method to state-of-the-art approaches in simulations using multiple public benchmark data sets. The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives. Our approach shows a marginal improvement over existing methods in the second setting and a significant improvement in the third.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 160, 21 July 2015, Pages 73–84
نویسندگان
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