Uses of Class
weka.classifiers.AbstractClassifier

Packages that use AbstractClassifier
  • Uses of AbstractClassifier in weka.classifiers

    Modifier and Type
    Class
    Description
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers.
    class 
    Abstract utility class for handling settings common to randomizable classifiers.
    class 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
    class 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
    class 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers based on a given random number seed.
    class 
    Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
    class 
    Abstract utility class for handling settings common to meta classifiers that use a single base learner.
  • Uses of AbstractClassifier in weka.classifiers.bayes

    Modifier and Type
    Class
    Description
    class 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
    class 
    Class for a Naive Bayes classifier using estimator classes.
    class 
    Class for building and using a multinomial Naive Bayes classifier.
    class 
    Multinomial naive bayes for text data.
    class 
    Class for building and using an updateable multinomial Naive Bayes classifier.
    class 
    Class for a Naive Bayes classifier using estimator classes.
  • Uses of AbstractClassifier in weka.classifiers.bayes.net

    Modifier and Type
    Class
    Description
    class 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
    class 
    Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format.

    For more details on XML BIF see:

    Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
    class 
    Bayes Network learning using various search algorithms and quality measures.
    Base class for a Bayes Network classifier.
  • Uses of AbstractClassifier in weka.classifiers.functions

    Modifier and Type
    Class
    Description
    class 
    * Implements Gaussian processes for regression without hyperparameter-tuning.
    class 
    Class for using linear regression for prediction.
    class 
    Class for building and using a multinomial logistic regression model with a ridge estimator.

    There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):

    If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.

    The probability for class j with the exception of the last class is

    Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

    The last class has probability

    1-(sum[j=1..(k-1)]Pj(Xi))
    = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

    The (negative) multinomial log-likelihood is thus:

    L = -sum[i=1..n]{
    sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
    +(1 - (sum[j=1..(k-1)]Yij))
    * ln(1 - sum[j=1..(k-1)]Pj(Xi))
    } + ridge * (B^2)

    In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
    class 
    A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
    class 
    Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
    class 
    Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
    class 
    Learns a simple linear regression model.
    class 
    Classifier for building linear logistic regression models.
    class 
    Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

    This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
    class 
    SMOreg implements the support vector machine for regression.
    class 
    Implementation of the voted perceptron algorithm by Freund and Schapire.
  • Uses of AbstractClassifier in weka.classifiers.lazy

    Modifier and Type
    Class
    Description
    class 
    K-nearest neighbours classifier.
    class 
    K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
    class 
    Locally weighted learning.
  • Uses of AbstractClassifier in weka.classifiers.meta

    Modifier and Type
    Class
    Description
    class 
    Class for boosting a nominal class classifier using the Adaboost M1 method.
    class 
    Meta classifier that enhances the performance of a regression base classifier.
    class 
    Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
    class 
    Class for bagging a classifier to reduce variance.
    class 
    Class for doing classification using regression methods.
    class 
    A metaclassifier that makes its base classifier cost sensitive.
    class 
    Class for performing parameter selection by cross-validation for any classifier.

    For more information, see:

    R.
    class 
    Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
    class 
    Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
    class 
    Class for performing additive logistic regression.
    class 
    A metaclassifier for handling multi-class datasets with 2-class classifiers.
    class 
    A metaclassifier for handling multi-class datasets with 2-class classifiers.
    class 
    Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
    class 
    Class for building an ensemble of randomizable base classifiers.
    class 
    Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
    class 
    This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
    class 
    A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
    class 
    Combines several classifiers using the stacking method.
    class 
    Class for combining classifiers.
    class 
    Generic wrapper around any classifier to enable weighted instances support.
    Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.
  • Uses of AbstractClassifier in weka.classifiers.misc

    Modifier and Type
    Class
    Description
    class 
    Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure.
    class 
    A wrapper around a serialized classifier model.
  • Uses of AbstractClassifier in weka.classifiers.pmml.consumer

    Modifier and Type
    Class
    Description
    class 
    Class implementing import of PMML General Regression model.
    class 
    Class implementing import of PMML Neural Network model.
    class 
    Abstract base class for all PMML classifiers.
    class 
    Class implementing import of PMML Regression model.
    class 
    Class implementing import of PMML RuleSetModel.
    class 
    Implements a PMML SupportVectorMachineModel
    class 
    Class implementing import of PMML TreeModel.
  • Uses of AbstractClassifier in weka.classifiers.rules

    Modifier and Type
    Class
    Description
    class 
    Class for building and using a simple decision table majority classifier.

    For more information see:

    Ron Kohavi: The Power of Decision Tables.
    class 
    This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
    class 
    Generates a decision list for regression problems using separate-and-conquer.
    class 
    Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
    class 
    Class for generating a PART decision list.
    class 
    Class for building and using a 0-R classifier.
  • Uses of AbstractClassifier in weka.classifiers.trees

    Modifier and Type
    Class
    Description
    class 
    Class for building and using a decision stump.
    class 
    A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time.
    class 
    Class for generating a pruned or unpruned C4.5 decision tree.
    class 
    Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
    class 
    M5Base.
    class 
    Class for constructing a forest of random trees.

    For more information see:

    Leo Breiman (2001).
    class 
    Class for constructing a tree that considers K randomly chosen attributes at each node.
    class 
    Fast decision tree learner.
  • Uses of AbstractClassifier in weka.classifiers.trees.lmt

    Modifier and Type
    Class
    Description
    class 
    Class for logistic model tree structure.
    class 
    Base/helper class for building logistic regression models with the LogitBoost algorithm.
  • Uses of AbstractClassifier in weka.classifiers.trees.m5

    Modifier and Type
    Class
    Description
    class 
    M5Base.
    class 
    This class encapsulates a linear regression function.
    class 
    Constructs a node for use in an m5 tree or rule