A Study on Symbolic Data Analysis.

符号数据分析研究。

• 批准号: 09680378
• 负责人: ICHINO Manabu
• 金额: $ 1.09万
• 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: 1997
• 资助国家: 日本
• 起止时间: 1997 至 1998
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09680378/
• 关键词: pattern recognition; data mining; symbolic data; feature selection; feature evaluation; interclass mutual neighborhood graph; mutual neighborhood graph; geometrical thickness; 相対近隣グラフ

A main theme in the term 1997-1998 is to establish the feature selection algorithm in classification problems. In our desired feature selection method, we have to evaluate a given feature subset simultaneously from the two viewpoints : the effectiveness of class discrimination ; and the effectiveness of the generality for class descriptions. For this purpose, we introduced a new graph concept so called the Interclass Mutual Neighborhood Graph (IMNG) and we constructed several new feature selection algorithms.Another research theme is to establish a feature selection method which is able to detect "geometrically thin structure in global sense" imbedded in multidimensional symbolic data In this research term, we found a sufficiently effective feature selection method which is based on a simple and intuitively clear principle that "If the given symbolic data has a functional structure, then the data has a geometrically thin structure". This method is useful and powerful as a preprocessing tool for functional identification problems by neural networks. A part of our results was reported in the international conference held at Luxembourg (KESDA-98) and was remarked as a new powerful engine for data mining.

1997-1998学期的一个主题是建立分类问题中的特征选择算法。在我们想要的特征选择方法中，我们必须从两个角度同时评估给定的特征子集：类别区分的有效性；以及类描述的通用性的有效性。为此，我们引入了一种新的图概念，称为类间互邻图（IMNG），并构建了几种新的特征选择算法。另一个研究主题是建立一种能够在全局中检测“几何薄结构”的特征选择方法。在这个研究术语中，我们发现了一种足够有效的特征选择方法，该方法基于一个简单且直观清晰的原理，即“如果给定的符号数据具有函数结构，则该数据具有几何稀疏性”。 结构”。该方法作为神经网络功能识别问题的预处理工具非常有用且强大。我们的部分成果在卢森堡举行的国际会议（KESDA-98）上进行了报告，并被评价为数据挖掘的新的强大引擎。

项目成果

Y.Ono and M.Ichino: "A new feature selection method to extract functional structures form multidimensional data" IEICE Trans.On Inform.And Systems. E81-D,6. 556-564 (1998)

Y.Ono 和 M.Ichino：“一种从多维数据中提取功能结构的新特征选择方法”IEICE Trans.On Inform.And Systems。

Y.Ono and M.Ichino: "A new feature selection method based on geometrical thickness." Research in Official Statistics,. vol.2(in press). (1998)

Y.Ono 和 M.Ichino：“一种基于几何厚度的新特征选择方法。”

Y.Ono and M.Ichino: "A new feature selection method to extract functional structures from multidimensional data" IEICE Trans. On Inform. and Systems,. E81-D, 6. 556-564 (1998)

Y.Ono 和 M.Ichino：“一种从多维数据中提取功能结构的新特征选择方法”IEICE Trans。

Y.Ono and M.Ichino: "A new feature selection method based on geometrical thickness" International Conference on Knowledge Extraction from Statistical Data (KESDA'98) Luxembourg. (1998)

Y.Ono 和 M.Ichino：“基于几何厚度的新特征选择方法”国际统计数据知识提取会议 (KESDA98) 卢森堡。

Y.Ono and M.Ichino: "A new feature selection method based on geometrical thickness" International Conference on Knowledge Extraction from Statistical Data(KESDA'98), Luxembourg. (1998)

Y.Ono 和 M.Ichino：“基于几何厚度的新特征选择方法”国际统计数据知识提取会议（KESDA98），卢森堡。