Combination of multiple classifiers with measurement values

An approach for the combination of classifiers, in the context that each classifier can offer not only class labels but also the corresponding measurement values, is introduced. This approach is called the Linear Confidence Accumulation method (LCA). The three steps that LCA consists of are: first, measurement values; second, a confidence aggregation function aggregates the confidence values of each class label; and the last, the final decision will be derived by a decision rule based on the accumulated confidence values. Preliminary experiments have been performed and showed that LCA achieved better performance than the voting and the Bayesian methods. This reveals that measurement values play an important role in improving a system´s performance when combining different classifiers.