Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin (Ivan Eggel) for a login account.
 [BibTeX] [RIS]
A DEXiRE for Extracting Propositional Rules from Neural Networks via Binarization
Type of publication: Article
Citation:
Journal: MDPI Electronics
Volume: 11
Number: 24
Year: 2022
Month: December
URL: https://www.mdpi.com/2079-9292...
DOI: https://doi.org/10.3390/electronics11244171
Abstract: Background: Despite the advancement in eXplainable Artificial Intelligence, the explanations provided by model-agnostic predictors still call for improvements (i.e., lack of accurate descriptions of predictors’ behaviors). Contribution: We present a tool for Deep Explanations and Rule Extraction (DEXiRE) to approximate rules for Deep Learning models with any number of hidden layers. Methodology: DEXiRE proposes the binarization of neural networks to induce Boolean functions in the hidden layers, generating as many intermediate rule sets. A rule set is inducted between the first hidden layer and the input layer. Finally, the complete rule set is obtained using inverse substitution on intermediate rule sets and first-layer rules. Statistical tests and satisfiability algorithms reduce the final rule set’s size and complexity (filtering redundant, inconsistent, and non-frequent rules). DEXiRE has been tested in binary and multiclass classifications with six datasets having different structures and models. Results: The performance is consistent (in terms of accuracy, fidelity, and rule length) with respect to the state-of-the-art rule extractors (i.e., ECLAIRE). Moreover, compared with ECLAIRE, DEXiRE has generated shorter rules (i.e., up to 74% fewer terms) and has shortened the execution time (improving up to 197% in the best-case scenario). Conclusions: DEXiRE can be applied for binary and multiclass classification of deep learning predictors with any number of hidden layers. Moreover, DEXiRE can identify the activation pattern per class and use it to reduce the search space for rule extractors (pruning irrelevant/redundant neurons)—shorter rules and execution times with respect to ECLAIRE.
Keywords: binary neural networks, eXplainable Artificial Intelligence (XAI), global explanations, rules extraction, rules induction
Authors Contreras, Victor H.
Marini, Niccolò
Fanda, Lora
Manzo, Gaetano
Mualla, Yazan
Calbimonte, Jean-Paul
Schumacher, Michael
Calvaresi, Davide
Added by: []
Total mark: 0
Attachments
  • electronics-11-04171.pdf
Notes
    Topics