A weight-adjusted-voting framework on an ensemble of classifiers for improving sensitivity

We propose a weight-adjusted-voting framework that combines an ensemble of classifiers for improving sensitivity of prediction. In this framework, we first adjust each individual classifier's weight in the ensemble based on their ability of making correct predictions, and then use the weight of classifiers and a voting strategy to make final predictions. We also propose a step-wise classifier selection approach and apply it in the weight-adjusted-voting framework to select the proper classifiers from all the candidate classifiers in an ensemble for better sensitivity. To compare the sensitivity of the proposed weight-adjusted-voting, and two other approaches of combining classifiers - voting, and stacking, as well as the sensitivity of each single classifier in the ensemble, we used two different datasets in the UCI machine learning repository for evaluation. The results have demonstrated that our weight-adjusted-voting framework performs better in sensitivity than other approaches compared in the experiment.