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A fuzzy approach to partitioning continuous attributes for classification

机译：一种将连续属性进行分类的模糊方法

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摘要

Classification is an important topic in data mining research. To better handle continuous data, fuzzy sets are used to represent interval events in the domains of continuous attributes, allowing continuous data lying on the interval boundaries to partially belong to multiple intervals. Since the membership functions of fuzzy sets can profoundly affect the performance of the models or rules discovered, the determination of membership functions or fuzzy partitioning is crucial. In this paper, we present a new method to determine the membership functions of fuzzy sets directly from data to maximize the class-attribute interdependence and, hence, improve the classification results. In other words, it forms a fuzzy partition of the input space automatically, using an information-theoretic measure to evaluate the interdependence between the class membership and an attribute as the objective function for fuzzy partitioning. To find the optimum of the measure, it employs fractional programming. To evaluate the effectiveness of the proposed method, several real-world data sets are used in our experiments. The experimental results show that this method outperforms other well-known discretization and fuzzy partitioning approaches.
机译：分类是数据挖掘研究中的重要课题。为了更好地处理连续数据，使用模糊集来表示连续属性域中的间隔事件，从而使位于间隔边界上的连续数据部分属于多个间隔。由于模糊集的隶属函数会深刻影响发现的模型或规则的性能，因此确定隶属函数或模糊划分至关重要。在本文中，我们提出了一种直接从数据中确定模糊集的隶属函数的新方法，以最大化类别-属性的相互依赖性，从而改善分类结果。换句话说，它使用信息理论方法来评估类成员资格和属性之间的相互依赖性，从而自动形成输入空间的模糊分区，该属性作为模糊分区的目标函数。为了找到最佳的测量方法，它采用分数编程。为了评估所提出方法的有效性，我们在实验中使用了几个实际数据集。实验结果表明，该方法优于其他众所周知的离散化和模糊划分方法。

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