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 |