The invention discloses an average error classification cost minimized classifier integrating method. The method comprises the following steps of: 1, acquiring a training sample set; 2, initializing a sample weight and assigning an initial value; 3, iterating for T times, and training to obtain T optimal weak classifiers, wherein the step 3 comprises the following sub-steps of: 31, training weak classifiers on the basis of the training sample set S with the weight; 32, regulating the sample weight according to the results of the step 31; 33, judging whether t is smaller than T, if so, making t equal to (t+1) and returning to the step 31, otherwise, entering a step 4; and 4, combining the T optimal weak classifiers to obtain the optimal combined classifier. Compared with the prior art, themethod has the advantages that: classification results can be gathered in a class with low error classification cost in real sense, and on the premise of not requiring the classifiers to be independent of one another directly, the training error rate is reduced along with the increase of the number of the trained classifiers and the problem that the classification results can be only gathered in a class with the lowest total error classification cost in the conventional cost-sensitive learning method is solved.