To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transforme...

Class imbalance (CI) is common in most non synthetic datasets, which presents a major challenge for many classification algorithms geared towards optimized overall accuracy whenever the minority class risk loss is often higher than the majority class one. Support vector machine (SVM), a machine learning (ML) technique deeply rooted in statistics, maximizes linear margins between classes and gen...

Learning in imbalanced datasets is a pervasive problem prevalent in a wide variety of real-world applications. In imbalanced datasets, the class of interest is generally a small fraction of the total instances, but misclassification of such instances is often expensive. While there is a significant body of research on the class imbalance problem for binary class datasets, multi-class datasets h...

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) ...

We propose a framework for classification learning based on generalized learning vector quantization using statistical quality measures as cost function. Statistical measures like the F -measure or the Matthews correlation coefficient reflect better the performance for two-class classification problems than the simple accuracy, in particular if the data classes are imbalanced. For this purpose,...

In this paper, we propose a new Random Forest (RF) based ensemble method, ForesTexter, to solve the imbalanced text categorization problems. RF has shown great success in many real-world applications. However, the problem of learning from text data with class imbalance is a relatively new challenge that needs to be addressed. A RF algorithm tends to use a simple random sampling of features in b...

Imbalanced class has been a common problem encountered in the modeling process, and attracted more attention from scholars. Biased classifiers, which limit classifiers' performance for minority classes, will be produced if imbalanced ratio between number of positive labels negative is ignored. The synthetic over-sampling technique (SMOTE) very classic popular method, widely used to address this...

Classification of heterogeneous classes present in the Hyperspectral image is one of the recent research issues in the field of remote sensing. This work presents a novel technique that classifies Hyperspectral images that contain number of classes by making use of the image moments. Recently, researchers have introduced a number of neural network models and structured output based methods for ...

Abstract The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies fo...

The classification of data with imbalanced class distributions has posed a significant drawback in the performance attainable by most well-developed classification systems, which assume relatively balanced class distributions. This problem is especially crucial in many application domains, such as medical diagnosis, fraud detection, network intrusion, etc., which are of great importance in mach...