The process of feature selection is generally used to minimize the size of dataset, to overcome the problem of over fitting and to increase the classifier efficiency. We proposed the JMIM i. e. Joint Mutual Information Maximization algorithm to extract feature and for creation of feature subset efficiently. These algorithms are based on joint mutual information. It follows maximum of minimum strategy. In this paper our aim is to work on utilization of JMIM algorithm, then we compare upcoming outcome with the previously highlighted problems in existed feature selection system. In utilization of JMIM algorithm, we are expecting that our simultaneous processing of feature set selection process will reduces time required for overall execution. As a part of our contribution the process distributed over different clouds that helps in execution and triggers the process.

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Mutual Information Feature Selection Classification Joint Mutual Information Parallel Computing.