Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imb...

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification...

We have seen the speed torque characteristic of the machine. In the stable region of operation in the motoring mode, the curve is rather steep and goes from zero torque at synchronous speed to the stall torque at a value of slip s = ŝ. Normally ŝ may be such that stall torque is about three times that of the rated operating torque of the machine, and hence may be about 0.3 or less. This means t...

In today’s era Heart Rate Variability becomes an important characteristic to determine the condition of heart. That’s why the calculation of HRV and classification to generate rules is necessary. Human Heart Generates the electrical signal. ECG is used to detect the heart beat. ECG signal contains lots of noise. To classify the signals first to decompose the signals using wavelet transform. Man...

We propose a new support vector machine (SVM) based method that improves the time series classification in magnetic resonance imaging (fMRI). We exploit the robust anisotropic diffusion (RAD) technique to increase the classification performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was cal...

In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings. (The true-positive rate is also known as sensitivity in biomedical informatics, ...

A fault-tolerant permanent-magnet vernier (FT-PMV) machine is designed for direct-drive applications, incorporating the merits of high torque density and high reliability. Based on the so-called magnetic gearing effect, PMV machines have the ability of high torque density by introducing the flux-modulation poles (FMPs). This paper investigates the fault-tolerant characteristic of PMV machines a...

The Validation of Answers has been seen recently as a classification problem able to introduce Machine Learning for improving Question Answering results. The unbalanced nature of collections has led to the use of measures based on precision and recall. However, Relative Operating Characteristic (ROC) analysis is preferred sometimes in similar classification tasks. In this article we compare bot...

Large scale machine learning produces massive datasets whose items are often associated with a confidence level and can thus be ranked. However, computing the precision of these resources requires human annotation, which is often prohibitively expensive and is therefore skipped. We consider the problem of cost-effectively approximating precisionrecall (PR) or ROC curves for such systems. Our no...

The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment....