# An Adaptive Weighted Deep Forest Classifier

## TL;DR

This paper introduces an adaptive weighted modification to the Deep Forest classifier that enhances its flexibility by assigning weights to training instances based on classification accuracy, improving performance over the original model.

## Contribution

It proposes a novel adaptive weighing mechanism for Deep Forest that incorporates confidence scores, making the classifier more flexible and boosting its effectiveness.

## Key findings

- The modified Deep Forest outperforms the original in experiments.
- Adaptive weighting improves classification accuracy.
- The approach is similar to AdaBoost in concept.

## 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.