Improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony Optimization

The traditional random forest algorithm works along with unbalanced data, cannot achieve satisfactory prediction results for minority class, and suffers from the parameter selection dilemma. In view of this problem, this paper proposes an unbalanced accuracy weighted random forest algorithm (UAW_RF) based on the adaptive step size artificial bee colony optimization. It combines the ideas of decision tree optimization, sampling selection, and weighted voting to improve the ability of stochastic forest algorithm when dealing with biased data classification. The adaptive step size and the optimal solution were introduced to improve the position updating formula of the artificial bee colony algorithm, and then the parameter combination of the random forest algorithm was iteratively optimized with the advantages of the algorithm. Experimental results show satisfactory accuracies and prove that the method can effectively improve the classification accuracy of the random forest algorithm.