A Deep Forest Improvement by Using Weighted Schemes

Abstract

A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The modification aims to increase the classification accuracy. It is carried out by assigning weights to training instances at each forest cascade level in accordance with their classification accuracy. Larger values of accuracy produce smaller weights. Two strategies for using the weights are considered. The first one when the weights are regarded as probabilities of choosing the corresponding instances in building decision trees. According to the second strategy, the weights are used in splitting rules. The modification increases the classification accuracy and may reduce the training time for many real datasets. Numerical experiments illustrate good performance of the proposed modification in comparison with the original Deep Forest proposed by Zhou and Feng.

Keywords

• machine learning
• classification
• deep forest
• AdaBoost