Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference
Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference
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DOI：
10.1109/mcdm.2009.4938841
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
Y. Nojima;H. Ishibuchi
中科院分区：
文献类型：
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作者：
Y. Nojima;H. Ishibuchi
从数值型数据中设计基于模糊规则的分类器可以看作是数据挖掘的一种方法。其主要特点是知识表示。每个模糊if-then规则的先行模糊集在语言上表示模式空间中的模糊区域。当用户的主要关注点是知识提取而不是分类器设计时，我们必须考虑两个相互冲突的目标：准确性最大化和可解释性最大化。正确分类的训练模式的数量通常用于准确性度量。另一方面，可解释性是非常主观的，如果没有用户的参与，很难定义。在本文中，我们将用户的偏好信息的多目标遗传模糊规则选择。我们提出了一个偏好函数组成的满意度函数的六个标准：平均置信度，平均覆盖率，使用的属性的数量，使用的粒度的最大数量，分类精度和规则的数量。由于很难事先直接定义每个满意度函数，因此在进化过程中由用户交互修改。将偏好函数作为多目标遗传模糊规则选择的目标函数之一。所提出的方法的有效性进行检查，通过案例研究，从皮马印度糖尿病数据的知识提取。
The design of fuzzy rule-based classifiers from numerical data can be regarded as one of data mining approaches. The main characteristic is its knowledge representation. Antecedent fuzzy sets of each fuzzy if-then rule linguistically represent a fuzzy region in the pattern space. When a user's main concern is knowledge extraction rather than classifier design, we have to consider two conflicting objectives: accuracy maximization and interpretability maximization. The number of correctly classified training patterns is often used for accuracy measure. On the other hand, interpretability is very subjective and hardly defined without the user's involvement. In this paper, we incorporate user's preference information into multiobjective genetic fuzzy rule selection. We propose a preference function composed of satisfaction level functions on six criteria: average confidence, average coverage, the number of used attributes, the maximum number of used granularities, classification accuracy, and the number of rules. Since it is hard to directly define each satisfaction level function beforehand, it is interactively modified by the user during the evolution. The preference function is handled as one of objective functions in multiobjective genetic fuzzy rule selection. The effectiveness of the proposed method is examined through a case study for knowledge extraction from the Pima Indian Diabetes data.