This paper presents a novel approach, called MODS, to build an accurate time evolving classifier from multiple one-class data streams learning time evolving classifier. Our proposed MODS approach works in two steps. In the first step, we first construct local one-class classifiers on the labeled positive examples from each sub-data stream respectively. We then collect the informative examples (...

Multiple Classifier System (MCS) is designed by combining two or more classifiers. MCS helps in increasing the accuracy of classification compared to the performance of the individual classifier. In this paper, Multiple Classifier System is implemented for automatic speech recognition. The combined classifier takes the final decision on predicted class label using a class label fuser (also call...

This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as “online RK-OC-ELM”. The baseline kernel hyperplane model considers whole data in a single chunk with regularized ELM approach for offline learning in case of one-class classification (OCC). Further, the basic hyper plane model is adapted in an online fashio...

A class-imbalanced classifier is a decision rule to predict the class membership of new samples from an available data set where the class sizes differ considerably. When the class sizes are very different, most standard classification algorithms may favor the larger (majority) class resulting in poor accuracy in the minority class prediction. A class-imbalanced classifier typically modifies a ...

This paper introduces a new cost function for evaluating the multi-class classifier. The new cost function facilitates both a way to visualize the performance (expected cost) of the multi-class classifier and a summary of the misclassification costs. This function overcomes the limitations of ROC in not being able to represent the classifier performance graphically when there are more than two ...