2.4.1. Confidence weighted voting (CWV) strategy
More specific predictions can be given by assembling scores from individual models. Limitations in individual models can be avoided by integrating scores from many models, resulting in higher overall accuracy. Models blended in this way usually perform at least as well as the best and sometimes better individual models. Another weighted majority voting form is Confidence Weighted Voting (CWV). However, rather than giving high weights to the nearest sensors, CWV gives higher weights to those sensors that are more likely to be right (i.e., with higher confidence of correctness). A distributed comparison of sensing results and neighbors that share overlapping coverage can be made for the confidence value of each sensor. For this study, a confidence-weighted voting strategy was used to combine the scores from individual NN, SVM, and QUEST algorithms (11, 14).
\begin{equation} \sum_{m=1}^{M}{p_{m,j}d_{m,j}=\max_{k=1}^{K}}p_{m,j}\ \left(\sum_{m=1}^{M}d_{m,k}\right)\nonumber \\ \end{equation}
\(p_{m,j}\) is the posterior likelihood for a given vector of predictor values calculated for the kth target group by the mth base model. M is the number of base models, and K is the number of target categories (15).