论文标题
通过基于模糊规则的框架，通过特定于类和规则的功能选择更好地了解课程，以及冗余控制
Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework
论文作者
论文摘要
最近，一些研究声称，使用特定类别的特征子集比使用单个功能子集来表示分类问题的数据提供了某些优点。与传统的特征选择方法不同，特定于类的特征选择方法为每个类选择一个最佳特征子集。通常，特定于类的特定特征选择（CSF）方法使用数据集的全部分配，这会导致类别不平衡，决策聚合和高计算开销。我们提出了一种嵌入基于模糊规则的分类器中的特定类特征选择方法，该方法不受与大多数现有类特异性方法相关的缺点。此外，我们可以通过在学习目标中添加合适的正规器来调整我们的方法来控制特定特定特征子集中的冗余水平。我们的方法导致涉及特定班级子集的特定班级规则。我们还提出了一个扩展程序，其中特定类的不同规则由不同的特征子集定义，以模拟类中不同的子结构。该方法的有效性已通过对三个合成数据集的实验进行了验证。
Recently, several studies have claimed that using class-specific feature subsets provides certain advantages over using a single feature subset for representing the data for a classification problem. Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class. Typically class-specific feature selection (CSFS) methods use one-versus-all split of the data set that leads to issues such as class imbalance, decision aggregation, and high computational overhead. We propose a class-specific feature selection method embedded in a fuzzy rule-based classifier, which is free from the drawbacks associated with most existing class-specific methods. Additionally, our method can be adapted to control the level of redundancy in the class-specific feature subsets by adding a suitable regularizer to the learning objective. Our method results in class-specific rules involving class-specific subsets. We also propose an extension where different rules of a particular class are defined by different feature subsets to model different substructures within the class. The effectiveness of the proposed method has been validated through experiments on three synthetic data sets.
