To solve the problem that test information in analog circuit diagnosis is always insufficient

a new algorithm of information fusion is presented to improve the diagnosis accuracy in analog circuit.Firstly

fault samples are transformed in different feature spaces

and the relative dominance class set for each feature space is computed by distribution entropy of mahalanobis distance.Subsequently

the relative dominance attribute reduction is defined to extract local prime separability information.And finally

the trained basic classifiers are ensembled by fuzzy integral.The fault instance indicates that the presented method can inprove diagnosis accuracy effectively