MultinformationHeuristic: Feature-clustering based on Mutual Information Computation...

In D2MCS: Data Driving Multiple Classifier System

Feature-clustering based on Mutual Information Computation theory.

Description

Performs the feature-clustering using MCC score. Valid for both bi-class and multi-class problems. Only valid for bi-class problems.

Super class

D2MCS::GenericHeuristic -> MultinformationHeuristic

Methods

Public methods

• MultinformationHeuristic$new()

• MultinformationHeuristic$heuristic()

• MultinformationHeuristic$clone()

---

Method new()

Empty function used to initialize the object arguments in runtime.

Usage

MultinformationHeuristic$new()

---

Method heuristic()

Mutinformation takes two random variables as input and computes the mutual information in nats according to the entropy estimator method.

Usage

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

Arguments

col1

A vector/factor denoting a random variable or a data.frame denoting a random vector where columns contain variables/features and rows contain outcomes/samples.

col2

An another random variable or random vector (vector/factor or data.frame).

column.names

An optional character vector with the names of both columns.

Returns

Returns the mutual information I(X;Y) in nats.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MultinformationHeuristic$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Dataset, mutinformation

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