Package

weka.filters.unsupervised.attribute

Synopsis

Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.

For more information, see:

Ying Yang, Geoffrey I. Webb: Proportional k-Interval Discretization for Naive-Bayes Classifiers. In: 12th European Conference on Machine Learning, 564-575, 2001.

Options

The table below describes the options available for PKIDiscretize.

Option	Description
attributeIndices	Specify range of attributes to act on. This is a comma separated list of attribute indices, with "first" and "last" valid values. Specify an inclusive range with "-". E.g: "first-3,5,6-10,last".
bins	Ignored.
desiredWeightOfInstancesPerInterval	Sets the desired weight of instances per interval for equal-frequency binning.
findNumBins	Ignored.
ignoreClass	The class index will be unset temporarily before the filter is applied.
invertSelection	Set attribute selection mode. If false, only selected (numeric) attributes in the range will be discretized; if true, only non-selected attributes will be discretized.
makeBinary	Make resulting attributes binary.
useEqualFrequency	Always true.

Capabilities

The table below describes the capabilites of PKIDiscretize.

Capability	Supported
Class	Relational class, Numeric class, Binary class, No class, Empty nominal class, Missing class values, Unary class, Nominal class, String class, Date class
Attributes	Binary attributes, String attributes, Nominal attributes, Missing values, Unary attributes, Relational attributes, Empty nominal attributes, Numeric attributes, Date attributes
Min # of instances	0

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