Distributed pattern recognition within DHT-based networks for imbalanced datasets

This paper studies the accuracy of a fully-distributed pattern recognition algorithm, namely the P2P-GN, with imbalanced datasets problem which is often neglected by most of the available distributed algorithms. A major distinction of the P2P-GN compared to the other approaches is that it forms a single global classifier, instead of building many local classifiers (one at every site). Fine-granularity components of the classifier are distributed across the network by using Distributed Hash Table (DHT) - which provides efficient linking to these components and ensures the system remains fully-distributed. Our experimental results also show that the P2P-GN can produce highly accurate results despite imbalanced data distribution compared to other distributed algorithms (Ivote-DPV, ensemble ID3, ensemble k-NN, and ensemble naive Bayes).