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Performance analysis of rough set ensemble of learning classifier systems with differential evolution based rule discovery

机译：基于差异进化的规则发现的学习分类器系统粗糙集集成性能分析

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摘要

Data mining, and specifically supervised data classification, is a key application area for Learning Classifier Systems (LCS). Scaling to larger classification problems, especially to higher dimensional problems, is a key challenge. Ensemble based approaches can be applied to LCS to address scalability issues. To this end a rough set based ensemble of LCS is proposed, which relies on a pre-processed feature partitioning step to train multiple LCS on feature subspaces. Each base classifier in the ensemble is a Michigan style supervised LCS. The traditional genetic algorithm based rule evolution is replaced by a differential evolution based rule discovery, to improve generalisation capabilities of LCS. A voting mechanism is then used to generate output for test instances. This paper describes the proposed ensemble algorithm in detail, and compares its performance with different versions of base LCS on a number of benchmark classification tasks. Analysis of computational time and model accuracy show the relative merits of the ensemble algorithm and base classifiers on the tested data sets. The rough set based ensemble learning approach and differential evolution based rule searching out-perform the base LCS on classification accuracy over the data sets considered. Results also show that small ensemble size is sufficient to obtain good performance.
机译：数据挖掘，尤其是监督数据分类，是学习分类器系统（LCS）的关键应用领域。扩展到更大的分类问题，尤其是到更高维度的问题，是一个关键的挑战。基于集成的方法可以应用于LCS，以解决可伸缩性问题。为此，提出了一种基于粗糙集的LCS集成，它依赖于预处理的特征划分步骤来在特征子空间上训练多个LCS。集成中的每个基本分类器都是密歇根州风格的LCS。基于传统遗传算法的规则进化被基于差分进化的规则发现所取代，以提高LCS的泛化能力。然后使用表决机制为测试实例生成输出。本文详细描述了提出的集成算法，并在许多基准分类任务上将其与不同版本的基础LCS的性能进行了比较。计算时间和模型准确性的分析显示了集成算法和基于测试数据集的分类器的相对优点。基于粗糙集的集成学习方法和基于差分进化的规则搜索在考虑的数据集上的分类精度方面优于基本LCS。结果还表明，较小的合奏大小足以获得良好的性能。

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  • 来源
    《Evolutionary Intelligence》 |2013年第2期|109-126|共18页
  • 作者

    Essam Debie; Kamran Shafi; Chris Lokan; Kathryn Merrick;

  • 作者单位

    School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy">(1);

    School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy">(1);

    School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy">(1);

    School of Engineering and Information Technology University of New South Wales Australian Defence Force Academy">(1);

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Learning classifier systems; Rough set theory; Differential evolution; Ensemble learning;

    机译：学习分类器系统;粗糙集理论;差异演化;合奏学习;

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