Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems
Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems
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DOI：
10.1007/s10015-009-0700-3
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发表时间：
2009-12
影响因子：
0.9
通讯作者：
Y. Nojima;H. Ishibuchi
中科院分区：
文献类型：
--
作者：
Y. Nojima;H. Ishibuchi
在基于模糊规则的系统设计中，我们有两个相互冲突的目标：准确性最大化和可解释性最大化。作为可解释性的衡量标准，文献中提出了一些标准。为了通过遗传算法自动找到准确的、可解释的模糊系统，这些准则大多被纳入适应度函数中。然而，可解释性是非常主观的，很少事先为任何用户定义。在本文中，我们提出了将用户偏好纳入多目标遗传模糊规则选择的模式分类问题。用户偏好用偏好函数表示，该函数在进化过程中根据用户的直接操作而变化。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。通过设计基于模糊规则的分类器的实例，验证了该方法的有效性。
In the design of fuzzy-rule-based systems, we have two conflicting objectives: accuracy maximization and interpretability maximization. As a measure of interpretability, a number of criteria have been proposed in the literature. Most of those criteria have been incorporated into fitness functions in order to automatically find accurate and interpretable fuzzy systems by genetic algorithms. However, interpretability is very subjective and is rarely defined for any users beforehand. In this article, we propose the incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems. User preference is represented by a preference function which is changeable according to the user’s direct manipulation during evolution. The preference function is used as one of the objective functions in multi-objective genetic fuzzy rule selection. The effectiveness of the proposed method is examined through some case studies for the design of fuzzyrule-based classifiers.