Adaptive Oversampling for Imbalanced Data Classification

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 combines the benefits of oversampling and active learning. Unlike traditional resampling methods which require preprocessing of the data, Virtual generates synthetic examples for the minority class during the training process, therefore it removes the need for an extra preprocessing stage. In the context of learning with Support Vector Machines, we demonstrate that Virtual outperforms competitive oversampling techniques both in terms of generalization performance and computational complexity.


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Ertekin Bolelli, Şeyda; Bottou, Leon; Giles, C Lee (2007-10-06)
This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate th...
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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-out...
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An Evolution Operator Approach which were developed to solve nonlinear O.D.E. systems, X=f(x), X(0)=£, is discussed for the linear systems X=AX where A is nxn symmetric matrix. In this approach, each component of the solution vector is represented as an action of evolution operator, exp(itL), on xj and then approximated by method of Moment using [N+1,N] Pade’ approximation. In applications, the most important part of this method is the computation of dynamical and spectral coefficients [6]. The recursive fo...
Citation Formats
Ş. Ertekin Bolelli, “Adaptive Oversampling for Imbalanced Data Classification,” 2013, vol. 264, Accessed: 00, 2020. [Online]. Available: