Knn

Purpose

K-nearest neighbor classifier.

Synopsis

pclass = knn(xref,xtest,k,options); %make prediction without model

pclass = knn(xref,xtest,options); %use default k

model = knn(xref,k,options) %create model

modelp = knn(xref,model,k,options) %apply model to xtest

modelp = knn(xtest,model,options) %apply model to xtest; predictions (equivalent to pclass) in modelp.classification.mostprobable.

Description

Performs kNN classification where the "k" closest samples in a reference set vote on the class of an unknown sample based on distance to the reference samples. If no majority is found, the unknown is assigned the class of the closest sample (see input options for other no-majority behaviors).

Inputs

• xref = a DataSet object of reference data,

• xtest = a DataSet object or Double containing the unknown test data.

Optional Inputs

• model = an optional standard KNN model structure which can be passed instead of xref (note order of inputs: (xtest,model) ) to apply model to test data.

• k = number of components {default = rank of X-block}.

Outputs

• pclass = the voted closest class, if a majority of nearest neighbors were of the same class, or the class of the closest sample, if no majority was found (Only returned if xtest is supplied).

• model = if no test data (xtest) is supplied, a standard model structure is returned which can be used with test data in the future to perform a prediction. Note that information about the classification of X-block samples is available in the classification field, described at Standard Model.

For more information on class predictions, see Sample Classification Predictions.

Options

• options = structure array with the following fields :

• display: [ 'off' | {'on'} ] governs level of display to screen.

• preprocessing: { [ ] } A cell containing a preprocessing structure or keyword (see PREPROCESS). Use {'autoscale'} to perform autoscaling on reference and test data.

• nomajority: [ 'error' | {'closest'} | class_number ] Behavior when no majority is found in the votes. 'closest' = return class of closest sample. 'error' = give error message. class_number (i.e. any numerical value) = return this value for no-majority votes (e.g. use 0 to return zero for all no-majority votes)

• strictthreshold: Probability threshold value to use in strict class assignment, see Sample_Classification_Predictions#Class_Pred_Strict. Default = 0.5.

See Also

analysis, cluster, dbscan, knnscoredistance, modelselector, plsda, simca, svmda