Abstract: To address the computational redundancy of multi-granularity scanning and the efficiency loss caused by feature dimension expansion in the original deep forest framework,this paper proposes a novel deep forest model－CDS-Forest－based on CNN feature enhancement and dynamic sparse cascading.The proposed model utilizes a lightweight convolutional neural network(CNN) for efficient hierarchical feature extraction,effectively replacing the computationally intensive multi-granularity scanning module.Furthermore,a dynamic sparse cascading mechanism is introduced to adaptively control model complexity and eliminate feature redundancy through selective feature integration.Experimental results on two benchmark datasets,MNIST and CIFAR-10,demonstrate the superiority of CDS-Forest.On the MNIST dataset,it achieves an accuracy of 99.14%,significantly outperforming the original gcForest(96.28%) and standard CNN(98.75%),while its training time is only 1/112 of gcForest.On the CIFAR-10 dataset,CDS-Forest achieves an accuracy of 88.76%,which is 0.48 percentage points and 4.1 percentage points higher than standard CNN and gcForest,respectively,with the training time reduced to 1/62 of gcForest and 1/3 of standard CNN.CDS-Forest model exhibits low hardware dependency and operates efficiently without high-performance GPUs,offering a cost-effective and low-deployment-cost solution.This work provides an efficient,user-friendly,and hardware-light non-neural deep learning alternative for image classification,enriching the integration of deep learning and ensemble methods.

Key words: Deep forest, CNN feature enhancement, Dynamic sparse cascading, Image classification, Feature extraction