Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
نویسندگان
چکیده
منابع مشابه
Margin-Based Over-Sampling Method for Learning from Imbalanced Datasets
Learning from imbalanced datasets has drawn more and more attentions from both theoretical and practical aspects. Over-sampling is a popular and simple method for imbalanced learning. In this paper, we show that there is an inherently potential risk associated with the oversampling algorithms in terms of the large margin principle. Then we propose a new synthetic over sampling method, named Mar...
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Learning classifiers from imbalanced or skewed datasets is an important topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of ...
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Classification is one of the most fundamental tasks in the machine learning and data-mining communities. One of the most common challenges faced when trying to perform classification is the class imbalance problem. A dataset is considered imbalanced if the class of interest (positive or minority class) is relatively rare as compared to the other classes (negative or majority classes). As a resu...
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ژورنال
عنوان ژورنال: Al-Mustansiriyah Journal of Science
سال: 2020
ISSN: 2521-3520,1814-635X
DOI: 10.23851/mjs.v31i2.740