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Package

weka.classifiers.lazy

Synopsis

Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.
Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).

For more info, see

Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.

C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..

Options

The table below describes the options available for LWL.

Option

Description

KNN

How many neighbours are used to determine the width of the weighting function (<= 0 means all neighbours).

classifier

The base classifier to be used.

debug

If set to true, classifier may output additional info to the console.

nearestNeighbourSearchAlgorithm

The nearest neighbour search algorithm to use (Default: LinearNN).

weightingKernel

Determines weighting function. [0 = Linear, 1 = Epnechnikov,2 = Tricube, 3 = Inverse, 4 = Gaussian and 5 = Constant. (default 0 = Linear)].

Capabilities

The table below describes the capabilites of LWL.

Capability

Supported

Class

Numeric class, Missing class values, Binary class, Date class, Nominal class

Attributes

Numeric attributes, Binary attributes, Missing values, Empty nominal attributes, Nominal attributes, Date attributes, Unary attributes

Min # of instances

0

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