Learning Classifier Systems (LCSs) are a group of rule-based evolutionary computation techniques, which have been frequently applied to data mining tasks. The LCSs’ rules are designed to be human-readable to enable the underlying knowledge to be investigated. However, the models for the majority of domains with high feature interaction contain a large number of rules that cooperatively represent the knowledge. However, the interaction between many rules is too complex to be comprehended by humans. Thus, it is hypothesized that translating the models’ underlying patterns into human-discernable visualizations will advance the understanding of the learned patterns and LCSs themselves. Interrogatable artificial Boolean domains with varying numbers of attributes are considered as benchmarks. Three new visualization techniques, termed as Feature Importance Map, Action-based Feature Importance Map, and Action-based Feature’s Average value Map, successfully produce interpretable results for all the complex domains tested. This includes both tracing the training progress and analyzing the trained models from LCSs. The visualization techniques’ ability to handle complex optimal solutions is observed for the 14-bits Majority-On problem, where the patterns from 6435 different cooperating rules were translated into human-discernable graphs.

学习分类器系统（LCS）是一组基于规则的进化计算技术，这些技术经常应用于数据挖掘任务。LCS的规则设计为人类可读的，以使基础知识能够得到调查。但是，具有高功能交互的大多数领域的模型都包含大量可共同代表知识的规则。但是，许多规则之间的交互太复杂，以至于人类无法理解。因此，假设将模型的基础模式转换为人类可分辨的可视化效果将促进对学习的模式和LCS本身的理解。具有不同数量属性的可查询人工布尔域被视为基准。三种新的可视化技术 称为功能重要性图，基于操作的功能重要性图和基于操作的功能的平均值图，可以成功地为所有测试的复杂域生成可解释的结果。这包括跟踪训练进度和分析来自LCS的训练模型。对于14位的多数开启问题，观察到了可视化技术处理复杂最佳解决方案的能力，该问题将来自6435个不同合作规则的模式转换为可分辨的图形。