We present an important improvement related to the computation and use of Mutual Information index in Pseudobagging, a technique that adapts “bagging” to unsupervised context. The Mutual Information index plays a key role in this technique, assessing the quality of a partition. We propose the use of such an index to improve the Pseudobagging voting scheme for determining the final partition of the data. Issues related to the estimation of Mutual Information index in the multivariate continuous case become crucial for the application of Pseudobagging to real data: we discuss some practical approaches to computation in this situation. Finally, experimental results are presented, related to application of new “pooled voting” scheme and to the evaluation of the impact of different computing methods for Mutual Information.
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