Confidence ratio affinity propagation in ensemble selection of neural network classifiers for distributed privacy-preserving data mining

We consider distributed privacy-preserving data mining in large decentralized data locations which can build several neural networks to form an ensemble. The best neural network classifiers are selected via the proposed confidence ratio affinity propagation in an asynchronous distributed and privacy-preserving computing cycle. Existing methods usually need a shared to all classifiers dataset, in order to examine the classification accuracy of each pair of classifiers. This process is neither distributed nor privacy-preserving. On the other hand in the proposed distributed privacy-preserving solution the classifiers validate each other in a local way. The training set of one classifier becomes the validation set of the other and vice versa and only partial sums of confidences for the correctly and the falsely classified examples are collected. By locally defining a confidence ratio between each pair of classifiers the well known affinity propagation algorithm finds the most representative ones. The construction is parallelizable and the cost is O(LN) for L classifiers and N examples. A-priori knowledge for the number of best classifiers is not required since in affinity propagation algorithm this number emerges automatically. Experimental simulations on benchmark datasets and comparisons with other pair-wise diversity based measures and other existing pruning methods are promising.