关键词: 随机森林, 因子分析, 分类, 回归, 特征重要性, 中医药信息学

Abstract: Affected by the imbalance of feature importance, random forests may randomly extract weak feature subsets to generate a “weak decision tree”, which leads to a decrease in the convergence speed of the model and a decrease in the performance of the model. In view of this, this paper proposes a random forest model of fusion factor analysis. The main innovation is to construct a feature set by factor analysis method, and then form a candidate subset of each split node according to the feature number and random extraction feature. Based on the model’s classification prediction, regression fitting, accuracy and running time of feature importance analysis, the overall performance of 9 UCI data comprehensive survey models is selected, and compared with decision trees and random forests. The results show that the random forest model of fusion factor analysis basically eliminates the decision tree with low accuracy, improves the accuracy and convergence speed, and is more generalized, which is more conducive to high-dimensional big data, feasible and effective.

Key words: random forest, factor analysis, classification, regression, importance of feature, traditional Chinese medicine informatics