Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern
Classification
- URL: http://arxiv.org/abs/2107.03423v1
- Date: Wed, 7 Jul 2021 18:14:50 GMT
- Title: Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern
Classification
- Authors: Gonzalo N\'apoles, Yamisleydi Salgueiro, Isel Grau, Maikel Leon
Espinosa
- Abstract summary: We propose an LTCN-based model 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.
Our interpretable model obtains competitive performance when compared to the state-of-the-art white and black boxes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 meaningful and 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 specific
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 an LTCN-based model 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
unique fixed points while introducing a deterministic learning method to
compute the learnable parameters. The simulations show that our interpretable
model obtains competitive performance when compared to the state-of-the-art
white and black boxes.
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