Indexing Rules in Rule Sets for Fast Classification
2016 (English)In: Proceedings of the International Conference on Artificial Intelligence and Robotics and the International Conference on Automation, Control and Robotics Engineering, Association for Computing Machinery (ACM), 2016Conference paper (Refereed)
Using sets of rules for classification of examples usually in- volves checking a number of conditions to see if they hold or not. If the rule set is large the time to make the classifica- tion can be lengthy. In this paper we propose an indexing algorithm to decrease the classification time when dealing with large rule sets. Unordered rule sets have a high time complexity when conducting classification; we hence con- duct experiments comparing our novel indexing algorithm with the standard way of classifying ensembles of unordered rule sets. The result of the experiment shows decreased clas- sification times for the novel method that are ranging from 0.6 to 0.8 of that of the standard approach averaged over all experimental datasets. This time gain is obtained while re- taining an accuracy ranging from 0.84 to 0.99 with regard to the standard classification method. The index bit size used with the indexing algorithm influence both the classification accuracy and time needed for conducting the classification task.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2016.
Approximation algorithms analysis, Rule learning, Expert systems
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-135428DOI: 10.1145/2952744.2952750ISBN: 978-1-4503-4235-3OAI: oai:DiVA.org:su-135428DiVA: diva2:1045212
ICAIR '16 2016, International Conference on Artificial Intelligence and Robotics, Kitakyushu, Japan, July 13 - 15, 2016