A scalable model for knowledge sharing based supervised learning using AdaBoost

In this paper, we introduce a new scalable platform for knowledge sharing based group learning in an adaptive boosting(AdaBoost) environment for supervised learning Though knowledge sharing has been an active area of research in semi supervised learning, the concept has not been explored thoroughly in supervised learning framework. In our proposed algorithm, several learner members are trained simultaneously on sub sets of original feature spaces. Every agent is trained using the same baseline algorithm such as Artificial Neural Network (ANN). In each knowledge sharing session the colony of agents calculates difficulty of each of the training samples and accordingly changes weight distribution over training data space based on a probabilistic metric. Finally, for classification purpose, the decisions of all the agents are conglomerated based on a novel variant of majority voting. Based on voting protocol, we present three different ensemble learning algorithms. Extensive simulations performed on samples from Color FERET and UCI databases reveal that our algorithm outperforms traditional non cooperative boosting algorithms and some recent variants of collaborative boosting algorithms in terms of training error convergence rate, classification accuracy, and resiliency against labeling noise. Furthermore, error-diversity relationships of the ensemble learners are investigated using Kappa-Error diagrams. The results are promising.