Discretize
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 |