Theoretical XCS parameter settings of learning accurate classifiers-Reference-Cited by-同舟云学术

Theoretical XCS parameter settings of learning accurate classifiers

Published:2017-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Nakata Masaya¹,Browne Will²,Hamagami Tomoki¹,Takadama Keiki³

Affiliation:

1. Yokohama National University, Yokohama, Japan

2. Victoria University of Wellington, Wellington, New Zealand

3. The University of Electro-Communications, Tokyo, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3071178.3071200

Reference14 articles.

1. M. V. Butz. XCSJava 1.0: An Implementation of the XCS classifier system in Java. Tec. report 2000. M. V. Butz. XCSJava 1.0: An Implementation of the XCS classifier system in Java. Tec. report 2000.

2. Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy

3. Toward a Theory of Generalization and Learning in XCS

4. M. V. Butz and S. W. Wilson. An Algorithmic Description of XCS. Soft Computing 6(3--4):144--153 2002. M. V. Butz and S. W. Wilson. An Algorithmic Description of XCS. Soft Computing 6(3--4):144--153 2002.

Cited by 32 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. XCS with dynamic sized experience replay for memory constrained applications;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

2. Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks;Information Fusion;2024-05

3. Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME;International Journal of Fuzzy Systems;2023-11-17

4. Exploring High-dimensional Rules Indirectly via Latent Space Through a Dimensionality Reduction for XCS;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

5. Towards physician's experience: Development of machine learning model for the diagnosis of autism spectrum disorders based on complex T ‐spherical fuzzy‐weighted zero‐inconsistency method;Computational Intelligence;2022-12-05