In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of im...

In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as the constituents. The dataset was taken from the Business Intelligenc...

This paper proposes a novel algorithm Virtual Instances Resampling Technique Using Active Learning (VIRTUAL) for class imbalance problem in Support Vector Machine (SVM) learning. In supervised learning, prediction performance of the classification algorithms deteriorate when the training set is imbalanced. Class imbalance problem occurs when at least one of the classes are represented by substa...

We report the experiments and procedures to successfully record and reconstruct coherent diffraction patterns at sub-10-nm resolution from noncrystalline samples by using synchrotron x rays with a wavelength of 2 Å. By employing the oversampling phasing method, we studied the quality of image reconstruction of experimental diffraction patterns as a function of the oversampling ratio ~a paramete...

In this letter, oversampling effect is analyzed when correlation (CORR) metric is used in the selected mapping (SLM) scheme with the presence of nonlineartiy. In general, 4 times oversampling is enough for estimating continuous signal. But, we can use 2 times oversampling with similar bit error rate (BER) performance. Therefore, we can reduce the computational complexity half roughly. Simulatio...

In this paper an analysis on the oversampling data recovery circuit is presented. The input waveform is assumed to be non-return-zero (NRZ) binary signals. A finite Markov chain model is used to evaluate the steady-state phase jitter performance. Theoretical analysis enables us to predict the input signal-to-noise ratio (SNR) versus bit error rate (BER) of the oversampling data recovery circuit...

Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space ineffective datasets with overlapping or small disjunction. Based on this, novel oversampling technique based manifold distance proposed, which new sample produced terms distances among neighbours space,...