Package

weka.attributeSelection

Synopsis

CfsSubsetEval :

Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.

Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.

For more information see:

M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.

Options

The table below describes the options available for CfsSubsetEval.

Option	Description
locallyPredictive	Identify locally predictive attributes. Iteratively adds attributes with the highest correlation with the class as long as there is not already an attribute in the subset that has a higher correlation with the attribute in question
missingSeperate	Treat missing as a separate value. Otherwise, counts for missing values are distributed across other values in proportion to their frequency.

Capabilities

The table below describes the capabilites of CfsSubsetEval.

Capability	Supported
Class	Missing class values, Numeric class, Nominal class, Date class, Binary class
Attributes	Empty nominal attributes, Nominal attributes, Numeric attributes, Unary attributes, Date attributes, Binary attributes, Missing values
Min # of instances	1