A Study on Fuzzy Symbolic Classifiers.

模糊符号分类器的研究。

• 批准号: 07680412
• 负责人: ICHINO Manabu
• 金额: $ 0.96万
• 依托单位: Tokyo Denki University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1995
• 资助国家: 日本
• 起止时间: 1995 至 1996
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680412/
• 关键词: fuzzy classifier; pattern recognition; symbolic data; feature selection; class description; symbolic pattern; Cartesian System Model; membership function; ファジイ・クラシファイア-; データ・アナリシス; 条件つき特徴; 知識獲得; 学習; fuzzy classifier; pattern recognition; symbolic data; feature selection; class description; symbolic pattern; Cartesian System Model; membership function; ファジイ・クラシファイア-; データ・アナリシス; 条件つき特徴; 知識獲得; 学習

The symbolic data analysis is a new research field which has been rapidly grown up guided by Professor E.Diday in France. In this field, the generalization of the data descriptions is one main stream, where each sample data may be described not only by numeric features but also by symbolic features.The "fuzzy symbolic pattern classifiers" in this study can treat symbolic patterns which are simultaneously described by unmeric features and symbolic features. The following result are obtained in this study :1.The approach treated here is based on the Cartesian system model (CSM) which is our own mathematical model to treat symbolic data. In this study we generalized the CSM itself in order to treat wider types of features used to describe sample patterns.2.When samples are described only by numeric features, there exist fuzzy classifiers based on distance functions defined on the given set of sample patterns. We generalized this approach by introducing the generalized Minkowski metrics defined on the CSM.3.As a different method, we developed region oriented fuzzy symbolic classifier in which each pattern class is described by "events" (rectangular regions in numeric feature cases) and the membership functions are defined between events and sample patterns. We found that, in this approach, "feature selection" is essentially important in order to take balance between the separability among patterm classes and the generality of class descriptions. The result was reported as an invited paper for the symbolic data analysis in Fifth Conference of the International Federation of Classification Societies (IFCS-96) held in Kobe.

符号数据分析是一个新的研究领域，在法国E.dday教授的指导下，已迅速成长。在该字段中，数据描述的概括是一个主要流，其中每个样本数据不仅可以由数字特征来描述，还可以通过符号特征来描述。这项研究中的“模糊符号模式分类器”可以处理符号模式，这些模式可以同时描述，这些模式是由无效特征和符号特征描述的。在本研究中获得以下结果：1。此处处理的方法基于笛卡尔系统模型（CSM），这是我们自己的数学模型来处理符号数据。在这项研究中，我们概括了CSM本身，以治疗用于描述样品模式的更广泛类型的特征。2。仅通过数字特征描述样品时，基于在给定的样本模式集中定义的距离函数中存在模糊分类器。我们通过引入CSM.3上定义的广义Minkowski指标来概括这种方法。作为一种不同的方法，我们开发了面向区域的模糊符号分类器，其中每个模式类别由“事件”（数字特征案例中的矩形区域）描述，并且在事件和样本模式之间定义了成员资格功能。我们发现，在这种方法中，“特征选择”对于为了在模式类别之间的可分离性与类描述的一般性之间的分离性之间取得平衡至关重要。在科比举行的国际分类社会联合会（IFCS-96）的第五次会议上，该结果是作为符号数据分析的邀请论文。