کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6856515 1437961 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dynamic ensemble selection for multi-class imbalanced datasets
ترجمه فارسی عنوان
انتخاب گروه دینامیک برای مجموعه داده های چند طبقه ای نامتعادل
کلمات کلیدی
طبقه بندی چند طبقه مجموعه داده های نامتعادل، سیستم طبقه بندی چندگانه، یادگیری گروهی تجزیه دودویی، تکنیک های ریزپرداخت،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Many real-world classification tasks suffer from the class imbalanced problem, in which some classes are highly underrepresented as compared to other classes. In this paper, we focus on multi-class imbalance problems which are considerably more difficult to address than two-class imbalanced problems. On this account, we develop a novel and effective procedure, called dynamic ensemble selection for multi-class imbalanced datasets (DES-MI), in which the competence of the candidate classifiers are assessed with weighted instances in the neighborhood. The proposed DES-MI consists of two key components: the generation of balanced training datasets and the selection of appropriate classifiers. To do so, we develop a preprocessing procedure to balance the training dataset which relies on random balance. To select the most appropriate classifiers in the scenario of multi-class imbalance problems, we propose a weighting mechanism to highlight the competence of classifiers that are more powerful in classifying examples in the region of underrepresented competence. We develop a thorough experimental study in order to verify the benefits of DES-MI in handling multi-class imbalanced datasets. The obtained results, supported by the proper statistical analysis, indicate that DES-MI is able to improve the classification performance for multi-class imbalanced datasets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 445–446, June 2018, Pages 22-37
نویسندگان
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