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Fine tuning run parameter values in rule-based machine learning

Moschoyiannis, Sotiris and Shcherbinin, Vasily (2019) Fine tuning run parameter values in rule-based machine learning In: 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019), 16-19 Sep 2019, Bolzano, Italy.

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Rule-based machine learning focuses on learning or evolving a set of rules that represents the knowledge captured by the system. Due to its inherent complexity, a certain amount of fine tuning is required before it can be applied to a particular problem. However, there is limited information available to researchers when it comes to setting the corresponding run parameter values. In this paper, we investigate the run parameters of Learning Classifier Systems (LCSs) as applied to single-step problems. In particular, we study two LCS variants, XCS for reinforcement learning and UCS for supervised learning, and examine the effect that different parameter values have on enhancing the model prediction, increasing accuracy and reducing the resulting rule set size.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Shcherbinin, Vasily
Date : 2019
Copyright Disclaimer : © Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at:
Uncontrolled Keywords : condition-action rules; Learning classifier systems; Reinforcement learning; Supervised learning; Fine tuning LCS
Related URLs :
Depositing User : Clive Harris
Date Deposited : 19 Sep 2019 07:29
Last Modified : 26 Sep 2019 09:51

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