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A STUDY OF FEATURE (ATTRIBUTE) SELECTION IN DATA MINING

数据挖掘中特征（属性）选择的研究

基本信息

批准号：
12680398
负责人：
MANABU Ichino
金额：
$ 1.02万
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：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680398/
关键词：
pattern recognition data mining feature selection neighborhood graph feature evaluation geometrical thickness analysis of variance Calhoun correlation coefficient 相関係数 0-1整数計画手書き文字認識分離能力記述の一般性

项目摘要

The purpose of this research is to develop some methods of feature (attribute) selection in data mining. We report the results for feature selection in classification problems. Then, we report a new correlation coefficient which is applicable to various nonlinear relationships between feature variables.1) Feature selection for classification problems When we have only a finitenumber of samples, the classification performance may not be improved by the addition of new features used to describe samples. This means that we have to strike a balance between the interclass distinguishability and the generality of class descriptions. We introduced two graphs: the generality or dered mutual neighborhood graph and the generality ordered interclass mutual neighborhood graph, then we dev eloped a feature selection algorithm based on the modified zero-one integer programmirig and it's simplified algorithm.2) Generalized correlation coefficientPearson's correlation coefficient is useful to detect causality between feature variables. However, this well known tool is not applicable to general nonlinear causal relations. If two feature variables follow to a functional structure, the sample distribution with respect to the feature variables has a geometrically thin structure. We developed a generalized correlation coefficient, called the Calhoun correlation coefficient. This new measure are able to evaluate various nonlinear functional relations and other geometrically this structures.

本研究的目的是发展数据挖掘中特征（属性）选择的一些方法。我们报告了分类问题中特征选择的结果。然后，我们报告了一个新的相关系数，它适用于各种特征变量之间的非线性关系。1)分类问题的特征选择当我们只有有限数量的样本时，添加用于描述样本的新特征可能无法提高分类性能。这意味着我们必须在类间的可区分性和类描述的通用性之间取得平衡。在引入广义有序互邻图和广义有序类间互邻图两种图的基础上，提出了一种基于改进的0 - 1整数规划及其简化算法的特征选择算法。广义相关系数皮尔逊相关系数用于检测特征变量之间的因果关系。然而，这个众所周知的工具并不适用于一般的非线性因果关系。如果两个特征变量遵循一个函数结构，则样本分布相对于特征变量具有几何薄结构。我们提出了一个广义的相关系数，叫做卡尔霍恩相关系数。这种新方法能够从几何上评价各种非线性函数关系和其他这种结构。