1 / 13
文档名称:

机器学习工具WEKA的使用总结,包括算法选择、属性选择、参数优化.pdf

格式:pdf   大小:510KB   页数:13页
下载后只包含 1 个 PDF 格式的文档,没有任何的图纸或源代码,查看文件列表

如果您已付费下载过本站文档,您可以点这里二次下载

文档介绍:Evaluation Warning : The document was created with Spire.PDF .

一、属性选择:
f an attribute by measuring the information gain with
respect to the class.
InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).(4)OneRAttributeEval

根据 OneR 分类器评估属性。
Class for building and using a 1R classifier; in other words, uses the
minimum-error attribute for prediction, discretizing numeric attributes. For more
information, see:
R.C. Holte (1993). Very simple classification rules perform well on most
commonly used datasets. Machine Learning. 11:63-91.

(5)PrincipalComponents

主成分分析(PCA)。
Performs a principal components analysis and transformation of the data. Use in
conjunction with a Ranker search. Dimensionality reduction is accomplished by
choosing enough eigenvectors to account for some percentage of the variance in the
original data---default 0.95 (95%). Attribute noise can be filtered by transforming to
the PC space, eliminating some of the worst eigenvectors, and then transforming back
to the original space.

(6)ReliefFAttributeEval

根据 ReliefF 值评估属性。
Evaluates the worth of an attribute by repeatedly sampling an instance and
considering the value of the given attribute for the nearest instance of the same and
different class.Can operate on both discrete and continuous class data.
For more information see:
Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In:
Ninth International Workshop on Machine Learning, 249-256, 1992.
Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In:
European Conference on Machine Learning, 171-182, 1994.
Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute
estimation in regression. In: Fourteenth International Conference on Machine
Learning, 296-304, 1997.(7)SymmetricalUncertAttributeEval

根据属性的对称不确定性评估属性。
Evaluates the worth of an attribute by measuring the

分享好友

预览全文

机器学习工具WEKA的使用总结,包括算法选择、属性选择、参数优化.pdf

上传人:guoxiachuanyue006 2022/5/20 文件大小:510 KB

下载得到文件列表

机器学习工具WEKA的使用总结,包括算法选择、属性选择、参数优化.pdf

相关文档