Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data.

中文翻译：

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具有统计和可视化引导知识发现的分析管道，用于密歇根式学习分类器系统

密歇根式学习分类器系统 (M-LCS) 代表了一类自适应且功能强大的进化算法，它将学习到的解决方案分布在大量规则上。然而，它们在复杂的现实世界数据挖掘问题（例如遗传关联研究）中的应用受到限制。M-LCS 规则种群的传统知识发现策略涉及排序和手动规则检查。虽然这种方法可能足以解决更简单的问题，但噪声的混杂影响以及区分预测和非预测属性的需要需要额外的策略。此外，重要性测试必须适用于 M-LCS 分析，以使它们成为需要此类分析来评估置信度的领域中的可行选择。在这项工作中，我们引入了 M-LCS 分析管道，该管道将独特应用的可视化与客观统计评估相结合，以识别预测属性，并在嘈杂的单步数据挖掘问题中进行可靠的规则概括。这项工作考虑了 M-LCS 中知识发现的另一种范式，将重点从单个规则转移到全球、全人群的视角。我们证明了该管道应用于识别嘈杂模拟遗传关联数据中的上位性（即属性相互作用）和异质性的功效。这项工作考虑了 M-LCS 中知识发现的另一种范式，将重点从单个规则转移到全球、全人群的视角。我们证明了该管道应用于识别嘈杂模拟遗传关联数据中的上位性（即属性相互作用）和异质性的功效。这项工作考虑了 M-LCS 中知识发现的另一种范式，将重点从单个规则转移到全球、全人群的视角。我们证明了该管道应用于识别嘈杂模拟遗传关联数据中的上位性（即属性相互作用）和异质性的功效。