Abstract
In this paper, we propose a feed-forward ensemble neural network for data sets having both discrete and continuous attributes. The ensemble provides results that are more accurate than those of conventional neural networks and expresses more comprehensible rules. Through the separation of data in compliance with primary rules, it enables the generation of secondary rules that apply solely to instances of non-compliance with the primary rules and maintain higher accuracy than is conventionally attainable. We demonstrate the high performance of the ensemble neural network with rules extracted by Re-RX, and verify that it can reduce the complexity of handling multiple neural networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
