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The majority of learning systems don't take in consideration real world data problem and consider that the training sets are perfect. However, in real world data, this hypothesis is not always true. In fact, real world data is characterized by many different problems like redundancy, incoherence or the big size of data. In this paper we focus on the problem of imbalance between class. Many solutions...
The majority of learning systems usually assume that training sets are balanced, however, in real world data this hypothesis is not always true. The problem of between-class imbalance is a challenge that has attracted growing attention from both academia and industry, because of its critical influence on the performance of learning systems. Many solutions were proposed to resolve this problem: Generally,...
The majority of Machine Learning (ML) habitually assume that the training sets used for learning are balanced. However, in real world application this hypothesis is not always true. The problem of between-class imbalance is a challenge that has attracted growing attention from both academia and industry because of his critical influence on the performance of machine learning. Many solutions are proposed...
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