The invention discloses a progressive ensemble classification method based on a kernel width learning system. The progressive ensemble classification method comprises the following steps: 1) inputtinga training sample and a test sample; 2) training a kernel width learning system as a base classifier by using the original training data; 3) calculating a prediction residual error according to the training result of the first base classifier, and taking the prediction residual error as a label trained by the next base classifier; 4) when the reduction rate of the trained loss function value reaches a threshold value, stopping the training, and not continuing to increase the base classifier any more; and 5) classifying the test samples to obtain a final prediction result. According to the method, the nonlinear fitting capability of the classifier is improved by introducing a kernel mapping technology while redundant back propagation is not needed by utilizing a width learning system, anda plurality of base classifiers are fused by using an integrated means, so that an obvious improvement effect is achieved on a biological information data set with noise, and the accuracy of biological gene classification is improved.
Progressive ensemble classification method based on kernel width learning system