Separating rule discovery and global solution composition in a learning classifier system

Heider, Michael; Stegherr, Helena; Wurth, Jonathan; Sraj, Roman; Hähner, Jörg

doi:10.1145/3520304.3529014

Cited by 8 publications

(1 citation statement)

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“…By default, SupRB employs an Evolution Strategy (ES) to discover locally optimal rules, preferring to generate rules in parts of the input space where the in-sample error of the current global solution is the highest. The process of how exactly rules are generated is not the focus of this paper, and details regarding the RD and the exact structure of rules can be found in [9]. The rules in the pool are nevertheless assumed to have the following properties:…”

Section: Model Selection In Suprbmentioning

confidence: 99%

Comparing different metaheuristics for model selection in a supervised learning classifier system

Wurth

Heider

Stegherr

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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In the context of tasks that highly involve human interaction and expert knowledge, i.e., operator guidance in manufacturing, the possibility of decision verifications by the user is a key requirement for inspiring confidence in a system and its predictions. Rule-based Machine Learning offers one way to create such systems, with Learning Classifier Systems being a family of algorithms whose models are by design human-interpretable. Obtaining a rule base as compact and accurate as possible is a mandatory prerequisite to increasing comprehensibility, and metaheuristics such as Genetic Algorithms or Particle Swarm Optimization are powerful approaches to reducing the size of large rule bases. In particular, this paper will analyze five population-based metaheuristics and their ability to compose solutions (rule subsets) as part of a newly developed rule-based learning system, the Supervised Rule-based Learning System (SupRB). The experiments suggest that all metaheuristics can significantly reduce the complexity and filter out obstructive rules, increasing the prediction quality in the process. CCS CONCEPTS• Computing methodologies → Supervised learning by regression; Rule learning; Genetic algorithms.

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Section: Model Selection In Suprbmentioning

confidence: 99%