Modifier and Type | Class and Description |
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class |
AODE
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes.
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class |
AODEsr
AODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification time and deletes the generalization attribute value.
For more information, see: Fei Zheng, Geoffrey I. |
class |
BayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
ComplementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier.
For more information see, Jason D. |
class |
DMNBtext
Class for building and using a Discriminative Multinomial Naive Bayes classifier.
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class |
NaiveBayes
Class for a Naive Bayes classifier using estimator classes.
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class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier.
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class |
NaiveBayesMultinomialUpdateable
Class for building and using a multinomial Naive Bayes classifier.
|
class |
NaiveBayesUpdateable
Class for a Naive Bayes classifier using estimator classes.
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Modifier and Type | Class and Description |
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class |
BayesNetGenerator
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
class |
BIFReader
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 |
EditableBayesNet
Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. |
Modifier and Type | Class and Description |
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class |
IsotonicRegression
Learns an isotonic regression model.
|
class |
LinearRegression
Class for using linear regression for prediction.
|
class |
Logistic
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 |
MultilayerPerceptron
A Classifier that uses backpropagation to classify instances.
This network can be built by hand, created by an algorithm or both. |
class |
PaceRegression
Class for building pace regression linear models and using them for prediction.
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class |
SimpleLinearRegression
Learns a simple linear regression model.
|
class |
SimpleLogistic
Classifier for building linear logistic regression models.
|
class |
SMO
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
SMOreg implements the support vector machine for regression.
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Modifier and Type | Class and Description |
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class |
IBk
K-nearest neighbours classifier.
|
class |
LWL
Locally weighted learning.
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Modifier and Type | Class and Description |
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class |
AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method.
|
class |
AdditiveRegression
Meta classifier that enhances the performance of a regression base classifier.
|
class |
AttributeSelectedClassifier
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
|
class |
Bagging
Class for bagging a classifier to reduce variance.
|
class |
LogitBoost
Class for performing additive logistic regression.
|
class |
MultiBoostAB
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. |
class |
RandomCommittee
Class for building an ensemble of randomizable base classifiers.
|
class |
RandomSubSpace
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 |
RotationForest
Class for construction a Rotation Forest.
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Modifier and Type | Class and Description |
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class |
MIOptimalBall
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
|
class |
MISMO
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. |
Modifier and Type | Class and Description |
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class |
VFI
Classification by voting feature intervals.
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Modifier and Type | Class and Description |
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class |
ConjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. |
class |
DecisionTable
Class for building and using a simple decision table majority classifier.
For more information see: Ron Kohavi: The Power of Decision Tables. |
class |
DTNB
Class for building and using a decision table/naive bayes hybrid classifier.
|
class |
JRip
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
|
class |
JRip.Antd
The single antecedent in the rule, which is composed of an attribute and
the corresponding value.
|
class |
JRip.NominalAntd
The antecedent with nominal attribute
|
class |
JRip.NumericAntd
The antecedent with numeric attribute
|
class |
JRip.RipperRule
This class implements a single rule that predicts specified class.
|
class |
PART
Class for generating a PART decision list.
|
class |
Ridor
An implementation of a RIpple-DOwn Rule learner.
It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. |
class |
Rule
Abstract class of generic rule
|
class |
ZeroR
Class for building and using a 0-R classifier.
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Modifier and Type | Class and Description |
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class |
ADTree
Class for generating an alternating decision tree.
|
class |
DecisionStump
Class for building and using a decision stump.
|
class |
J48
Class for generating a pruned or unpruned C4.5 decision tree.
|
class |
J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
|
class |
NBTree
Class for generating a decision tree with naive Bayes classifiers at the leaves.
For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. |
class |
RandomForest
Class for constructing a forest of random trees.
For more information see: Leo Breiman (2001). |
class |
RandomTree
Class for constructing a tree that considers K
randomly chosen attributes at each node.
|
class |
REPTree
Fast decision tree learner.
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Modifier and Type | Class and Description |
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class |
FTInnerNode
Class for Functional Inner tree structure.
|
class |
FTLeavesNode
Class for Functional Leaves tree version.
|
class |
FTNode
Class for Functional tree structure.
|
class |
FTtree
Abstract class for Functional tree structure.
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Modifier and Type | Class and Description |
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class |
LMTNode
Class for logistic model tree structure.
|
class |
LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm.
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Modifier and Type | Class and Description |
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class |
EM
Simple EM (expectation maximisation) class.
EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. |
class |
MakeDensityBasedClusterer
Class for wrapping a Clusterer to make it return a distribution and density.
|
class |
SimpleKMeans
Cluster data using the k means algorithm
Valid options are:
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Modifier and Type | Class and Description |
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class |
Discretize
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes.
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Modifier and Type | Class and Description |
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class |
PKIDiscretize
Discretizes numeric attributes using equal frequency binning, where the number of bins is equal to the square root of the number of non-missing values.
For more information, see: Ying Yang, Geoffrey I. |
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