Online extreme learning machine for handling concept drift and class imbalance problem

Abstract

The key interest of machine learning is conventionally training the machine from data that have underlying distribution such as data should have predetermined distribution. Such a constraint on the problem area leads to the technique for development of learning algorithms with notionally verifiable performance accuracy. However, real-world problems are not able to fit smartly into such restricted model. Class imbalance problem can occur due to tilted distribution of class data. Data streaming from non-stationary distribution with more uncertainty in real-time applications, resulting in the concept drift problem. In this methodology, it is proposed to extend the Extreme Learning Machine (ELM) algorithm for effectively handling the class imbalance problem and concept drift in datasets. This proposal has higher level of prediction accuracy and performance compared to Support Vector Machine (SVM) and Support Vector Data Description.