کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
409283 679064 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Addressing imbalance in multilabel classification: Measures and random resampling algorithms
ترجمه فارسی عنوان
مقابله با عدم تعادل در طبقه بندی چندزبانه: اندازه گیری و الگوریتم های نمونه گیری تصادفی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

The purpose of this paper is to analyze the imbalanced learning task in the multilabel scenario, aiming to accomplish two different goals. The first one is to present specialized measures directed to assess the imbalance level in multilabel datasets (MLDs). Using these measures we will be able to conclude which MLDs are imbalanced, and therefore would need an appropriate treatment. The second objective is to propose several algorithms designed to reduce the imbalance in MLDs in a classifier-independent way, by means of resampling techniques. Two different approaches to divide the instances in minority and majority groups are studied. One of them considers each label combination as class identifier, whereas the other one performs an individual evaluation of each label imbalance level. A random undersampling and a random oversampling algorithm are proposed for each approach, giving as result four different algorithms. All of them are experimentally tested and their effectiveness is statistically evaluated. From the results obtained, a set of guidelines directed to show when these methods should be applied is also provided.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 163, 2 September 2015, Pages 3–16
نویسندگان
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