Uses of Class
meka.classifiers.multilabel.CC

Packages that use CC

Package	Description
meka.classifiers.multilabel
meka.classifiers.multilabel.incremental
meka.classifiers.multitarget
meka.core

Uses of CC in meka.classifiers.multilabel

Subclasses of CC in meka.classifiers.multilabel

Modifier and Type	Class and Description
class	BCC BCC.java - Bayesian Classifier Chains.
class	CT CT - Classifier Trellis.
class	MCC MCC.java - CC with Monte Carlo optimisation.
class	PCC PCC.java - (Bayes Optimal) Probabalistic Classifier Chains.
class	PMCC PMCC.java - Like MCC but creates a population of M chains at training time (from Is candidate chains, using Monte Carlo sampling), and uses this population for inference at test time; If you are looking for a 'more typical' majority-vote ensemble method, use something like EnsembleML or BaggingML with MCC.

Methods in meka.classifiers.multilabel with parameters of type CC

Modifier and Type	Method and Description
double	MCC.likelihood(CC h, weka.core.Instances D) Likelihood - Return a default score of h evaluated on D.
double	MCC.likelihood(CC h, weka.core.Instances D, int payoff_fn) Likelihood - Return a score of choice (payoff_fn) of h evaluated on D.

Uses of CC in meka.classifiers.multilabel.incremental

Subclasses of CC in meka.classifiers.multilabel.incremental

Modifier and Type	Class and Description
class	CCUpdateable CCUpdateable.java - Updateable version of CC.

Uses of CC in meka.classifiers.multitarget

Subclasses of CC in meka.classifiers.multitarget

Modifier and Type	Class and Description
class	CC
class	CCp CCp.java - Multitarget CC with probabilistic output.

Uses of CC in meka.core

Methods in meka.core that return CC

Modifier and Type	Method and Description
static CC	CCUtils.buildCC(int[] chain, weka.core.Instances D, weka.classifiers.Classifier g) BuildCC - Given a base classifier 'g', build a new CC classifier on data D, given chain order 'chain'.