Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification

Abstract

Among different machine learning algorithms AdaBoost is a classification technique, which improves the classification accuracy by increasing the weights of the misclassified data. To overcome the problem of misclassification in Real AdaBoost algorithm, of the already classified samples, concept of margin is employed in the Parameterized AdaBoost algorithm. The new parameter, introduced in Parameterized AdaBoost, corresponding to the margin is chosen randomly between 0 to 1. However, the margin value is different for different classification problem. Hence, in this paper, the parameter corresponding to the margin is adapted by learning the parameter value with the help of Differential Evolutionary algorithm corresponding to the optimal classification accuracy. Experiment for the support of the proposed Adaptive Parameterized AdaBoost Algorithm has been conducted with different standard database given by UCI Machine Learning Repository. In addition, an application of Adaptive Parameterized AdaBoost is performed in EEG Motor Imagery Classification. Finally the EEG Motor Imagery Classified data (Left/Right) is tested in a robot.