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Tham khảo tài liệu 'computational intelligence in automotive applications by danil prokhorov_12', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | An Integrated Diagnostic Process for Automotive Systems 209 Here Nk is the number of training samples from class Ck L is the number of classifiers. The class with the highest support is declared as the winner. Fusion of Classifier Output Ranks. The output of classifiers can be a ranking of the preferences over the C possible output classes. Several techniques operating on this type of output are discussed below. 1 Borda Count The ranked votes from each classifier are assigned weights according to their rank. The class ranked first is given a weight of C the second a weight of C 1 and so on until a weight of 1 is assigned for the class ranked last. The score for each class is computed as the sum of the class weights from each classifier and the winner is the class with the highest total weight 31 . 2 Ranked Pairs Ranked Pairs is a voting technique where each voter participates by listing his her preference of the candidates from the most to the least preferred. In a ranked pair election the majority preference is sought as opposed to the majority vote or the highest weighted score. That is we combine the outputs of classifiers to maximize the mutual preference among the classifiers. This approach assumes that voters have a tendency to pick the correct winner 31 . This type of fusion as in majority voting does not require any training. If a crisp label is required as a final output the first position in the ranked vector RV is provided as the final decision. Fusion of Classifier Posterior Probabilities. The output of a classifier can be an array of confidence estimates or posterior probability estimates. These estimates represent the belief that the pattern belongs to each of the classes. The techniques in this section operate on the values in this array to produce a final fusion label. 1 Bayesian Fusion Class-specific Bayesian approach to classifier fusion exploits the fact that different classifiers can be good at classifying different fault classes. The .
