An ensemble classification algorithm for convolutional neural network based on AdaBoost
Shuo Yang, Lifang Chen, Tao Yan, Yaoyao Zhao, Ye-Jia Fan · 2017
AdaBoost is a classic ensemble learning algorithm with good classifier performance. In the past, it mainly used weak classifier as base classifier, such as KNN. They are simple and easy to train, but the essence of the weak classifier, it is impossible to get very high classification accuracy. In order to improve the correct rate, this paper introduces the AdaBoost ensemble classifier based on convolutional neural network, namely adaBoost-CNN, referred to as ACNN. ACNN design a new training method, it not only gives the weight of base classifier according to the error rate of base classifier in pre-training phase, but also dynamically adjusts this weight and learning coefficient of training sample according to the error rate of each class in ensemble training phase. Finally, through experiments on some public datasets, it was proved that ACNN not only can effectively reduce the classification error rate, but also can solve the problem of class recognition rate imbalanced caused by similar categories or training samples quantity deviation.