Abstract:Most of the traditional classifications algorithms have the same classification cost of all categories, which results in a sharp decline in classification performance when the sample data are unbalanced. As to the problem of unbalanced data classification, we combine neural network with denoising auto-encoder and put forward a kind of improved neural network to realize unbalanced data classification algorithm. The algorithm adds a layer called feature damaged layer between input layer and hidden layer. Thus some redundant feature values are lost, and the unbalance degree of data set is reduced. And the results can be obtained after training model obtains optimal parameters and deals with the classification based on feature. It selects three sets of UCI standard unbalanced data sets for experiment. The results show that the accuracy of the algorithm for small data set classification is improved obviously, but when the data set is larger, the classification effect is lower than some classifier. And the overall classification performance of the proposed algorithm is better than other classifiers.