.png){kind=icon}
.png){kind=icon}
Return
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2022.3165104.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 article, we propose a long-term cognitive network (LTCN) 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.
Keywords:
Computational modeling
Predictive models
Neurons
Mathematical models
Data models
Cognition
Numerical models
Explainable artificial intelligence
long-term cognitive networks (LTCNs)
machine-learning interpretability
recurrent neural networks
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
View more organizations