Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

TL;DR

This paper introduces a recurrence-aware long-term cognitive network that offers intrinsic interpretability for pattern classification, providing explanations and competitive performance compared to existing models.

Contribution

The paper presents a novel interpretable neural network model with recurrence-awareness and a deterministic learning algorithm, enhancing transparency without sacrificing accuracy.

Findings

Competitive results against state-of-the-art models

Effective feature relevance quantification for explanations

Robust handling of recurrent neural network issues

Abstract

Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose a Long-Term Cognitive Network for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models.