Package

weka.filters.supervised.attribute

Synopsis

An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani's MDL method (the default).

For more information, see:

Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, 1022-1027, 1993.

Igor Kononenko: On Biases in Estimating Multi-Valued Attributes. In: 14th International Joint Conference on Articial Intelligence, 1034-1040, 1995.

Options

The table below describes the options available for Discretize.

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".
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.
useBetterEncoding	Uses a more efficient split point encoding.
useKononenko	Use Kononenko's MDL criterion. If set to false uses the Fayyad & Irani criterion.

Capabilities

The table below describes the capabilites of Discretize.

Capability	Supported
Class	Nominal class, Binary class
Attributes	Empty nominal attributes, String attributes, Date attributes, Nominal attributes, Numeric attributes, Binary attributes, Relational attributes, Missing values, Unary attributes
Min # of instances	0