An Adaptive Weighted Deep Forest Classifier

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 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.

DADFN: dynamic adaptive deep fusion network based on imaging genomics for prediction recurrence of lung cancer

“…Only informative features were used for each level of learning, which greatly saved time cost and memory requirements. Utkin et al (2019) proposed a gcForest algorithm with the idea of AdaBoost. Each layer of gcForest cannot only extract the feature information of the data, but also use the wrongly classified samples to update the sampling weight of the samples, and then update the data distribution of the training data set of each layer, so that subsequent layers pay more attention to misclassified samples.…”