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Package

weka.filters.unsupervised.attribute

Synopsis

Replaces missing numeric values using Expectation Maximization with a multivariate normal model. Described in " Schafer, J.L. Analysis of Incomplete Multivariate Data, New York: Chapman and Hall, 1997."

Available in Weka 3.7.0 - 3.7.1. Available via the package management system for Weka >= 3.7.2 (EMImputation)

Options

The table below describes the options available for EMImputation.

Option

Description

debug

Turns on output of debugging information.

logLikelihoodThreshold

Log-likelihood threshold for convergence in Expectation Maximization. If the change in the observed data log-likelihood across iterations is no more than this value, then convergence is considered to be achieved and the iterative process is ceased. (default = 0.0001)

numIterations

Maximum number of iterations for Expectation Maximization. EM is used to initialize the parameters of the multivariate normal distribution. (-1 = no maximum)

ridge

Ridge parameter for ridge prior.

useRidgePrior

Use a ridge prior instead of noninformative prior.

Capabilities

The table below describes the capabilites of EMImputation.

Capability

Supported

Class

Empty nominal class, Unary class, Missing class values, String class, Relational class, Nominal class, Date class, No class, Numeric class, Binary class

Attributes

Missing values, Numeric attributes

Min # of instances

0

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