The domain of machine learning and pattern recognition comes forth with number of algorithms and techniques for classification, recognition and information retrieval tasks. Although the techniques or algorithms
the under/oversampling techniques have been widely used to reduce and enlarge the numbers of data samples in the majority and minority classes, respectively. Moreover, the combinations of certain sampling approaches with ensemble classifiers have shown reasonably good performance. In this paper, a nov...
Various techniques in machine learning have been used for building software defect prediction (SDP) models to identify the defective software modules. Howe
In contrast, models built on the imbalanced training dataset were not good at detecting positive cases of preeclampsia. We can conclude that while imbalanced training datasets can be addressed by using oversampling and undersampling techniques before building prediction models, an improvement in model ...
Re-sampling techniques are divided in two categories: Under-sampling the majority class(es). Over-sampling the minority class. Combining over- and under-sampling. Create ensemble balanced sets. Below is a list of the methods currently implemented in this module. ...
(over-sampling). Despite the advantage of balancing classes, these techniques also have their weaknesses (there is no free lunch). The simplest implementation of over-sampling is to duplicate random records from the minority class, which can cause overfitting. In under-sampling, the simplest ...
tion.Inbrief,theprocessofclassificationanalysisisincludedinthe followingsteps: 1.Samplecollection. 2.Selectsamplesandattributesfortraining. 3.Trainaclasspredictingsystemusingtrainingsamples. 4.Usethepredictingsystemtoforecasttheclassofincoming samples. Theclassificationtechniquesusuallyassumethatthetraining sample...
From the fast development of Generative Adversarial Network (GAN), many studies based deep learning techniques have been performed to obtain super-resolution quality for real-time MRI reconstruction [22,23,24,25,26]. However, in comparison with the CS, these advanced methods are considered as ...
In general, the problem of classifying imbalanced data is due to the lack of training with a few minority class samples which are inadequate to predict accurately [5]. Previous studies have proposed resampling techniques to solve the problem of class imbalance. These techniques are mainly categorize...
In third place, cost-sensitive approaches [10], [17] combine the mentioned techniques in such a way that they incorporate different misclassification costs for the instances in the learning algorithm. Finally, ensemble solutions [12], [49], [47] try to combine one of the previous approaches ...