Combination of multiple classifiers with measurement values

This paper introduces one approach f o r the combination of classifiers, in a context that each classifier can ofler not only class labels but also Ihe corresponding measurement values. This approach is called the Linear Confidence Accumvlation method (LCA). The three steps that LCA consists of are: first, measurement values are transformed into confidence values; second, a confidence aggregation function aggregates the confidence values of each class label; and the last, the final decision will be derived b y 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 daflerent classifiers.