Building Decision Forest via Deep Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning approach to construct decision forests by modeling the process as a multi-agent Markov decision process, achieving competitive results on balanced and imbalanced datasets.

Contribution

It presents the first method to build decision forests by maximizing long-term rewards using deep reinforcement learning with a multi-agent system.

Findings

Performs comparably to random forest, AdaBoost, and GBDT on balanced datasets.

Outperforms traditional methods on imbalanced datasets.

Uses a decentralized partial observable Markov decision process model.

Abstract

Ensemble learning methods whose base classifier is a decision tree usually belong to the bagging or boosting. However, no previous work has ever built the ensemble classifier by maximizing long-term returns to the best of our knowledge. This paper proposes a decision forest building method called MA-H-SAC-DF for binary classification via deep reinforcement learning. First, the building process is modeled as a decentralized partial observable Markov decision process, and a set of cooperative agents jointly constructs all base classifiers. Second, the global state and local observations are defined based on informations of the parent node and the current location. Last, the state-of-the-art deep reinforcement method Hybrid SAC is extended to a multi-agent system under the CTDE architecture to find an optimal decision forest building policy. The experiments indicate that MA-H-SAC-DF has the same performance as random forest, Adaboost, and GBDT on balanced datasets and outperforms them on imbalanced datasets.