关键词: 神经网络分类器, MLP回归预测模型, 特征丢失, 随机森林算法

Abstract: Aiming at the problem of the loss of features and precision degradation in the analysis process of neural network classifier prediction model based on dimension reduction, we proposed a multi-layer perceptron (MLP) regression prediction model optimized by random forest algorithm. The optimization model added an optimization mechanism between the full connection layer and the logistic regression layer of MLP regression network, the random forest algorithm was used to optimize the state of hidden layer, so as to solve the problem of losing some data features in the process of dimension reduction of neural network. The experimental results on the information data set of the borrowing customers show that the model can guarantee the main features and greatly improve the prediction accuracy, which proves that the model has high practicability in feature engineering.

Key words: neural network classifier, MLP regression prediction model, feature loss, random forest algorithm