smile.classification

Class OneVersusRest<T>

java.lang.Object

smile.classification.OneVersusRest<T>

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<T>, java.util.function.ToIntFunction<T>, Classifier<T>, SoftClassifier<T>

public class OneVersusRest<T>
extends java.lang.Object
implements SoftClassifier<T>

One-vs-rest (or one-vs-all) strategy for reducing the problem of multiclass classification to multiple binary classification problems. It involves training a single classifier per class, with the samples of that class as positive samples and all other samples as negatives. This strategy requires the base classifiers to produce a real-valued confidence score for its decision, rather than just a class label; discrete class labels alone can lead to ambiguities, where multiple classes are predicted for a single sample.

Making decisions means applying all classifiers to an unseen sample x and predicting the label k for which the corresponding classifier reports the highest confidence score.

Although this strategy is popular, it is a heuristic that suffers from several problems. Firstly, the scale of the confidence values may differ between the binary classifiers. Second, even if the class distribution is balanced in the training set, the binary classification learners see unbalanced distributions because typically the set of negatives they see is much larger than the set of positives.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
OneVersusRest(Classifier<T>[] classifiers, PlattScaling[] platts) Constructor.
OneVersusRest(Classifier<T>[] classifiers, PlattScaling[] platts, smile.util.IntSet labels) Constructor.

Method Summary

Modifier and Type	Method and Description
static <T> OneVersusRest<T>	fit(T[] x, int[] y, java.util.function.BiFunction<T[],int[],Classifier<T>> trainer) Fits a multi-class model with binary classifiers.
static <T> OneVersusRest<T>	fit(T[] x, int[] y, int pos, int neg, java.util.function.BiFunction<T[],int[],Classifier<T>> trainer) Fits a multi-class model with binary classifiers.
int	predict(T x) Predicts the class label of an instance.
int	predict(T x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, f, predict

Constructor Detail

OneVersusRest

public OneVersusRest(Classifier<T>[] classifiers,
                     PlattScaling[] platts)

Constructor.

Parameters:

classifiers - the binary classifier for each one-vs-rest case.

OneVersusRest

public OneVersusRest(Classifier<T>[] classifiers,
                     PlattScaling[] platts,
                     smile.util.IntSet labels)

Constructor.

Parameters:

classifiers - the binary classifier for each one-vs-rest case.

labels - the class labels.

Method Detail

fit

public static <T> OneVersusRest<T> fit(T[] x,
                                       int[] y,
                                       java.util.function.BiFunction<T[],int[],Classifier<T>> trainer)

Fits a multi-class model with binary classifiers. Use +1 and -1 as positive and negative class labels.

Parameters:

x - the training samples.

y - the training labels.

trainer - the lambda to train binary classifiers.

fit

public static <T> OneVersusRest<T> fit(T[] x,
                                       int[] y,
                                       int pos,
                                       int neg,
                                       java.util.function.BiFunction<T[],int[],Classifier<T>> trainer)

Fits a multi-class model with binary classifiers.

Parameters:

x - the training samples.

y - the training labels.

pos - the class label for one case.

neg - the class label for rest cases.

trainer - the lambda to train binary classifiers.

predict

public int predict(T x)

Description copied from interface: Classifier

Predicts the class label of an instance.

Specified by:

predict in interface Classifier<T>

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

predict

public int predict(T x,
                   double[] posteriori)

Description copied from interface: SoftClassifier

Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.

Specified by:

predict in interface SoftClassifier<T>

Parameters:

x - an instance to be classified.

posteriori - the array to store a posteriori probabilities on output.

Returns:

the predicted class label