Artificial Neural Network Rule Extraction Apparatus and Method

Explainable AI technology for extracting the decision basis of artificial neural networks as logical rules

This technology relates to an artificial neural network rule extraction apparatus and method. Specifically, it is an explainable AI technology that extracts the decision basis of artificial neural networks into logical rules.

Existing technologies faced limitations with traditional methods and required a more efficient and accurate approach, especially in extracting rules from artificial neural networks for data with continuous attributes. To address this, the present technology proposes a configuration that includes an artificial neural network rule extractor for learning input datasets, comprising a data learning unit, a first rule extraction unit, a binary classification unit, and a second rule extraction unit.

Accordingly, this technology can improve the accuracy and efficiency of extracting rules from artificial neural networks for data with continuous attributes, overcome the limitations of traditional methods, and provide a more efficient and accurate approach. It has value for utilization in the software, IT, and internet fields.

‍

‍

Key Features

• Implements explainable AI technology to extract the decision basis of artificial neural networks as logical rules
• Extracts neural network learning results in the form of binary classification rules and logical rules
• Improves the accuracy and efficiency of extracting rules from artificial neural networks for data with continuous attributes, overcomes the limitations of traditional methods, and provides a more efficient and accurate approach
• Applicable for advancing products and services in the software and IT infrastructure fields

‍