Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search

Author:

Heider MichaelORCID,Stegherr Helena,Pätzel David,Sraj Roman,Wurth Jonathan,Volger Benedikt,Hähner Jörg

Abstract

AbstractAutomated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and $$(1, \lambda )$$ ( 1 , λ ) -evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than $$(1, \lambda )$$ ( 1 , λ ) -ES which allows a good balance between low error and low complexity in the resulting models.

Funder

Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie

Deutsche Forschungsgemeinschaft

Universität Augsburg

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Cited by 3 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. A Closer Look at Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

2. A Survey on Learning Classifier Systems from 2022 to 2024;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

3. Measuring Similarities in Model Structure of Metaheuristic Rule Set Learners;Lecture Notes in Computer Science;2024

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3