smile.imputation

Class KNNImputation

java.lang.Object

smile.imputation.KNNImputation

All Implemented Interfaces:

MissingValueImputation

public class KNNImputation
extends Object
implements MissingValueImputation

Missing value imputation by k-nearest neighbors. The KNN-based method selects instances similar to the instance of interest to impute missing values. If we consider instance A that has one missing value on attribute i, this method would find K other instances, which have a value present on attribute 1, with values most similar (in term of some distance, e.g. Euclidean distance) to A on other attributes without missing values. The average of values on attribute i from the K nearest neighbors is then used as an estimate for the missing value in instance A. In the weighted average, the contribution of each instance is weighted by similarity between it and instance A.

Author:

Haifeng Li

Constructor Summary

Constructors

Constructor and Description
KNNImputation(int k) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	impute(double[][] data) Impute missing values in the dataset.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KNNImputation

public KNNImputation(int k)

Constructor.

Parameters:

k - the number of neighbors used for imputation.

Method Detail

impute

public void impute(double[][] data)
            throws MissingValueImputationException

Description copied from interface: MissingValueImputation

Impute missing values in the dataset.

Specified by:

impute in interface MissingValueImputation

Parameters:

data - a data set with missing values (represented as Double.NaN). On output, missing values are filled with estimated values.

Throws:

MissingValueImputationException