Book ChapterDOI
DeepRED – Rule Extraction from Deep Neural Networks
Jan Ruben Zilke,Eneldo Loza Mencía,Frederik Janssen +2 more
- pp 457-473
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TLDR
A new decompositional algorithm – DeepRED – is introduced that is able to extract rules from deep neural networks that are easy to understand and understandable.Abstract:
Neural network classifiers are known to be able to learn very accurate models. In the recent past, researchers have even been able to train neural networks with multiple hidden layers (deep neural networks) more effectively and efficiently. However, the major downside of neural networks is that it is not trivial to understand the way how they derive their classification decisions. To solve this problem, there has been research on extracting better understandable rules from neural networks. However, most authors focus on nets with only one single hidden layer. The present paper introduces a new decompositional algorithm – DeepRED – that is able to extract rules from deep neural networks.read more
Citations
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Journal ArticleDOI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Posted Content
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
Proceedings ArticleDOI
Explaining Explanations: An Overview of Interpretability of Machine Learning
TL;DR: In an effort to create best practices and identify open challenges, the authors describe foundational concepts of explainability and show how they can be used to classify existing literature, and discuss why current approaches to explanatory methods especially for deep neural networks are insufficient.
Proceedings ArticleDOI
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda
TL;DR: This work investigates how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers.
Posted Content
Explaining Explanations: An Overview of Interpretability of Machine Learning
TL;DR: In an effort to create best practices and identify open challenges, the authors provide a definition of explainability and show how it can be used to classify existing literature, and discuss why current approaches to explanatory methods especially for deep neural networks are insufficient.
References
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Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Journal ArticleDOI
Survey and critique of techniques for extracting rules from trained artificial neural networks
TL;DR: This survey focuses on mechanisms, procedures, and algorithms designed to insert knowledge into ANNs, extract rules from trained ANNs (rule extraction), and utilise ANNs to refine existing rule bases (rule refinement).
Proceedings Article
Extracting Tree-Structured Representations of Trained Networks
Mark Craven,Jude W. Shavlik +1 more
TL;DR: This work presents a novel algorithm, TREPAN, for extracting comprehensible, symbolic representations from trained neural networks, which is general in its applicability and scales well to large networks and problems with high-dimensional input spaces.
Journal ArticleDOI
Extracting Refined Rules from Knowledge-Based Neural Networks
TL;DR: This article proposes and empirically evaluates a method for the final, and possibly most difficult, step of the refinement of existing knowledge and demonstrates that neural networks can be used to effectively refine symbolic knowledge.