کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
392291 664755 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Semi-supervised classification method through oversampling and common hidden space
ترجمه فارسی عنوان
روش طبقه بندی نیمه نظارت شده از طریق فراوانی و فضای مخفی مشترک
کلمات کلیدی
طبقه بندی نیمه نظارت، بیش از اندازه، فضای مخفی مشترک، افزایش ابعاد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Semi-supervised classification methods attempt to improve classification performance based on a small amount of labeled data through full use of abundant unlabeled data. Although existing semi-supervised classification methods have exhibited promising results in many applications, they still have drawbacks, including performance degeneration, due to the introduction of unlabeled data and partially false labels in a small amount of labeled data. To circumvent such drawbacks, a new semi-supervised classification method OCHS-SSC through oversampling and a common hidden space is proposed in the paper. The primary characteristics of the proposed method include two aspects. One is that unlabeled data are only used to generate new synthetic data to extend the minimal amount of labeled data. The other is that the final classifier is learned in the extended feature space, which is composed of the original feature space and the common hidden space found between labeled data and the synthetic data instead of the original feature space. Extensive experiments on 23 datasets indicate the effectiveness of the proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 349–350, 1 July 2016, Pages 216–228
نویسندگان
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