Deep Forest with Hashing Screening and Window Screening

TL;DR

This paper introduces HW-Forest, a deep learning model that uses hashing and window screening strategies to reduce redundancy in feature vectors, leading to faster processing and higher accuracy.

Contribution

The paper proposes HW-Forest, combining hashing and window screening to efficiently eliminate redundant features and improve deep forest performance without extensive hyperparameter tuning.

Findings

HW-Forest reduces time cost and memory usage.

It achieves higher accuracy than comparable models.

The self-adaptive window screening improves robustness across datasets.

Abstract

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.