文档介绍:: .
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:
. 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 (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 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 measur