InformationGainHeuristic: Feature-clustering based on InformationGain methodology.

In D2MCS: Data Driving Multiple Classifier System

Feature-clustering based on InformationGain methodology.

Description

Performs the feature-clustering using entropy-based filters.

Super class

D2MCS::GenericHeuristic -> InformationGainHeuristic

Methods

Public methods

• InformationGainHeuristic$new()

• InformationGainHeuristic$heuristic()

• InformationGainHeuristic$clone()

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Method new()

Empty function used to initialize the object arguments in runtime.

Usage

InformationGainHeuristic$new()

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Method heuristic()

The algorithm find weights of discrete attributes basing on their correlation with continuous class attribute. Particularly Information Gain uses H(Class) + H(Attribute) - H(Class, Attribute)

Usage

InformationGainHeuristic$heuristic(col1, col2, column.names = NULL)

Arguments

col1

A numeric vector or matrix required to perform the clustering operation.

col2

A numeric vector or matrix to perform the clustering operation.

column.names

an optional character vector with the names of both columns.

Returns

A numeric vector of length 1 or NA if an error occurs.

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Method clone()

The objects of this class are cloneable with this method.

Usage

InformationGainHeuristic$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dataset, information.gain

D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.