Minimum Rule-Repair Algorithm for Supervised Learning Classifier Systems on Real-Valued Classification Tasks

Hamasaki, Koki; Nakata, Masaya

doi:10.1007/978-3-030-94216-8_11

Cited by 1 publication

(1 citation statement)

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“…To improve the performance of UCS on real-valued classification tasks Hamasaki and Nakata [12] propose the Minimum Rulerepair Algorithm (MRA). This algorithm attempts to slightly modulate the decision boundary so that misclassified examples are no longer matched without overly reducing rule generality.…”

Section: Improvements For Ucsmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs. CCS CONCEPTS• Computing methodologies → Rule learning; Genetic algorithms; • General and reference → Surveys and overviews.

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Section: Improvements For Ucsmentioning

confidence: 99%