Bigot, Samuel ![]() |
Abstract
Over the past few decades, several covering algorithms have been developed to automate the acquisition of knowledge from a set of examples. These algorithms employ a specific search process for extracting IF–THEN rules. They rely heavily on statistical measures to guide the search for rules; however, the information carried by these measures is limited and does not always lead to the best results. This paper presents two new algorithms which employ a new knowledge representation scheme to optimize the search and reduce the role of statistical measures, namely RULES-5 Plus for classification problems (discrete outputs) and RULES-F Plus for control applications (numerical outputs).
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC) |
Subjects: | T Technology > T Technology (General) |
Uncontrolled Keywords: | rule induction; fuzzy system; concept learning; search strategy; classification; control |
Publisher: | Institution of Mechanical Engineers |
ISSN: | 0959-6518 |
Last Modified: | 18 Oct 2022 14:23 |
URI: | https://orca.cardiff.ac.uk/id/eprint/17313 |
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