Sieve: An Ensemble Algorithm Using Global Consensus for Binary Classification

Abstract

In the field of machine learning, an ensemble approach is often utilized as an effective means of improving on the accuracy of multiple weak base classifiers. A concern associated with these ensemble algorithms is that they can suffer from the Curse of Conflict, where a classifier’s true prediction is negated by another classifier’s false prediction during the consensus period. Another concern of the ensemble technique is that it cannot effectively mitigate the problem of Imbalanced Classification, where an ensemble classifier usually presents a similar magnitude of bias to the same class as its imbalanced base classifiers. We proposed an improved ensemble algorithm called “Sieve” that overcomes the aforementioned shortcomings through the establishment of the novel concept of Global Consensus. The proposed Sieve ensemble approach was benchmarked against various ensemble classifiers, and was trained using different ensemble algorithms with the same base classifiers. The results demonstrate that better accuracy and stability was achieved.

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“…To sum up, our voting methods were influenced by the number of classifiers with a lower performance opposed to SVM. Hence, the accuracy of the ensembles are lower than the best base classifier, probably due to the 'curse of conflict' problem [104].…”