An Adaptive Weighted Deep Forest Classifier
This is an incremental improvement for machine learning practitioners using Deep Forest classifiers.
The authors tackled the problem of improving the Deep Forest classifier by proposing an adaptive weighted modification to the confidence screening mechanism, which assigns weights to training instances based on classification accuracy rather than removing them. Numerical experiments showed the proposed modification achieved good performance compared to the original Deep Forest.
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 idea underlying the modification is very simple and stems from the confidence screening mechanism idea proposed by Pang et al. to simplify the Deep Forest classifier by means of updating the training set at each level in accordance with the classification accuracy of every training instance. However, if the confidence screening mechanism just removes instances from training and testing processes, then the proposed modification is more flexible and assigns weights by taking into account the classification accuracy. The modification is similar to the AdaBoost to some extent. Numerical experiments illustrate good performance of the proposed modification in comparison with the original Deep Forest proposed by Zhou and Feng.