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Publication . Conference object . Part of book or chapter of book . 2018

A Rule Extraction Study Based on a Convolutional Neural Network

Guido Bologna;
Open Access
English

doi: 10.1007/978-3-319-99740-7_22

Published: 27 Aug 2018
Publisher: HAL CCSD
Abstract

Part 5: MAKE Explainable AI; International audience; Convolutional Neural Networks (CNNs) lack an explanation capability in the form of propositional rules. In this work we define a simple CNN architecture having a unique convolutional layer, then a Max-Pool layer followed by a full connected layer. Rule extraction is performed after the Max-Pool layer with the use of the Discretized Interpretable Multi Layer Perceptron (DIMLP). The antecedents of the extracted rules represent responses of convolutional filters, which are difficult to understand. However, we show in a sentiment analysis problem that from these “meaningless” values it is possible to obtain rules that represent relevant words in the antecedents. The experiments illustrate several examples of rules that represent n-grams.

Subjects by Vocabulary

Microsoft Academic Graph classification: Layer (object-oriented design) Discretization Multilayer perceptron Convolutional neural network Sentiment analysis Artificial intelligence business.industry business Pattern recognition Simple (abstract algebra) Computer science

Subjects

Convolutional Neural Networks, Rule extraction, Sentiment analysis, [INFO]Computer Science [cs], [SHS.INFO]Humanities and Social Sciences/Library and information sciences, [INFO] Computer Science [cs], [SHS.INFO] Humanities and Social Sciences/Library and information sciences

Related Organizations
14 references, page 1 of 2

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