Evolutionary Multi-Objective Optimization of Fuzzy Rule-Based Classifiers in the ROC Space

M. Cococcioni, P. Ducange, B. Lazzerini, F. Marcelloni
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引用次数: 5

Abstract

An approach to select the most suitable fuzzy rule-based binary classifier to a specific application is proposed. First, an evolutionary three-objective optimization algorithm is applied to generate an approximation of a Pareto front composed of fuzzy rule-based binary classifiers with different trade-offs between accuracy and complexity. Accuracy is measured in terms of sensitivity and specificity, whereas complexity is computed as sum of the conditions which compose the antecedents of the rules included in the classifiers. Thus, low values of complexity correspond to fuzzy systems characterized by a low number of rules and a low number of input variables actually used in each rule. This ensures a high comprehensibility of the classifiers. Then, the most suitable classifier is selected by using the ROC convex hull method. We discuss the application of the proposed approach to generate a classifier for discriminating lung nodules from non-nodules in a computer aided diagnosis (CAD) system. Results obtained on a real data set extracted from lung CT images are also discussed
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ROC空间中模糊规则分类器的进化多目标优化
提出了一种针对具体应用选择最合适的模糊规则二分类器的方法。首先,采用一种进化三目标优化算法,生成由精度和复杂度权衡不同的模糊规则二元分类器组成的Pareto前沿逼近。准确性是根据敏感性和特异性来衡量的,而复杂性是根据构成分类器中包含的规则的先决条件的总和来计算的。因此,较低的复杂性值对应于模糊系统,其特征是规则数量较少,并且每个规则实际使用的输入变量数量较少。这确保了分类器的高度可理解性。然后,利用ROC凸包法选择最合适的分类器。我们讨论了该方法在计算机辅助诊断(CAD)系统中用于区分肺结节和非结节的分类器的应用。本文还讨论了从肺部CT图像中提取的真实数据集的结果
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