Interpretable Neural Networks with Random Constructive Algorithm

TL;DR

This paper presents an Interpretable Neural Network (INN) that uses spatial information and geometric strategies to improve interpretability, convergence, and performance, including a lightweight version for large-scale data.

Contribution

It introduces a novel INN framework with spatial and geometric strategies, and a lightweight variant, enhancing interpretability and efficiency over existing neural networks.

Findings

INN demonstrates superior speed and accuracy on benchmark datasets.

The geometric relationship strategy improves network convergence.

The lightweight INN is effective for large-scale data modeling.

Abstract

This paper introduces an Interpretable Neural Network (INN) incorporating spatial information to tackle the opaque parameterization process of random weighted neural networks. The INN leverages spatial information to elucidate the connection between parameters and network residuals. Furthermore, it devises a geometric relationship strategy using a pool of candidate nodes and established relationships to select node parameters conducive to network convergence. Additionally, a lightweight version of INN tailored for large-scale data modeling tasks is proposed. The paper also showcases the infinite approximation property of INN. Experimental findings on various benchmark datasets and real-world industrial cases demonstrate INN's superiority over other neural networks of the same type in terms of modeling speed, accuracy, and network structure.