Linear combination of weak estimators over fuzzy classifiers?
You might find a solution for this by checking out Viola &amp; Jones face detection algorithm (and object detection in general) http://www.cs.ubc.ca/~lowe/425/slides/13-ViolaJones.pdf. Particularly the AdaBoost algorithm for building a strong classifier from weak classifiers. https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf

In this algorithm this is used particularly for feature selection, where it is wanted to select just the features that grouped together classify better, discarding the other as noisy features.

The approach to obtain this strong classifier is having a set of examples X, a set of weights W and a set of expected results (classifications) Y. For a two-class example (e.g. face o no face image) the first weak classifier is selected and then the classification error is found between this classifier and the expected output. For the samples that were misclassified their weights are incremented, so the next weak classifier to find is more biased to neglect this wrong classification.

The algorithm (AdaBoost) converges when the sign of the sum of classifications for each sample outputs the correct classification.

For example:

Y = +1 -1 +1 +1

WC1=+1 +1 +1 +1
WC2=-1 -1 -1 -1
WC3=+1 -1 +1 +1


So the strong classifier is WC1+WC2+WC3:

SC=+1 -1 +1 +1 == Y


Hope this solves your question.