Improved algorithm of decision tree based on neural network

Abstract

Decision trees have been applied to solve many data mining problems due to their superior learning and classification capabilities, and have achieved good results. However, for dealing with big data and complex model problems, decision trees show insufficient accuracy and overfitting. In order to solve these problems, neural network is introduced as a decision tree node, and an improved algorithm based on neural network decision tree is proposed. In the neural network decision tree, the classifier learning consists of two stages: the first stage uses a heuristic algorithm with reduced uncertainty to divide the big data, and stops the growth of the decision tree until the node dividing ability is below a certain threshold; in the second stage, the neural network is used to classify the decision-making leaf node with generalization ability. The experimental results show that compared with the traditional classification learning algorithm, the algorithm has a higher accuracy rate and it can determine the size of decision tree through structural adaptation for the classification problem of identifying big data and complex patterns.