DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets
نویسندگان
چکیده
منابع مشابه
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a ...
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Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification a...
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Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications. SMOTE algorithm and its variations generate synthetic samples along a line segment that joins minority class instances. In th...
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Data imbalance is known to significantly hinder the generalization performance of supervised learning algorithms. A common strategy to overcome this challenge is synthetic oversampling, where synthetic minority class examples are generated to balance the distribution between the examples of the majority and minority classes. We present a novel adaptive oversampling algorithm, VIRTUAL, that comb...
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Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalanced classification has been a hot topic in the academic community. From data level to algorithm level, a lot of solutions have been proposed to tackle the problems resulted from imbalanced datasets. SMOTE is the most popular data-level method and a lot of derivations based on it are developed to ...
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ژورنال
عنوان ژورنال: Information
سال: 2020
ISSN: 2078-2489
DOI: 10.3390/info11120557