Information Fusion (Ensemble System) In Data Warehousing & Data Mining

Machine based systems can't keep up with the task of organizing the data in an up-to-date manner unless and until the data acquired is being planned or scheduled and managed in an appropriate manner. Today's datasets start as small chunk of information and grow exponentially over a period of time. Once the size is extremely large it becomes difficult to make decisions and to predict consistently and correctly from the datasets. Most of predictions do not hold true, if proper balancing and diversification in terms of certain conditions and parameters is not done. The present state has focused public attention in terms of making and combining the predictions from the available data i.e. analyzing the current (and past) data to make predictions with increasing predictive accuracy of the overall system. So, keeping these considerations in mind there is a need for the better concept (component) for Information Fusion to combine it with a solid theory in support and foundation. AdaBoost could be very useful with feature selection, especially when considering that it has solid theoretical foundation. Here, Genetic Algorithms are being used to select relevant features from large datasets along with Evaluation techniques. This can further be enhanced by using multiclassifier approach. The central objective is to develop the system that provides approximately 3-5% performance improvement at least over similar existing techniques.