|
Classifiers
Name |
Publication |
Description |
Attribute |
Class |
Papers |
Bayes |
AODE |
2005 |
Improved Naive Bayes |
unary, binary, nominal |
binary, nominal |
|
AODEsr |
2006 |
Improved AODE |
unary, binary, nominal |
binary, nominal |
|
BayesianLogisticRegression |
2004 |
Bayesian Logistic Regression |
unary, binary, numeric |
binary |
|
BayesNet |
|
A "framework" class that can construct different Bayesian Network classifiers with different estimators and search algorithms. |
unary, binary, nominal, numeric |
binary, nominal |
2, 5 |
ComplementNaiveBayes |
2003 |
Naive Bayes variant |
numeric |
binary, nominal |
|
DMNBText |
2008 |
Discriminative Multinominal Naive Bayes |
numeric |
binary, nominal |
|
HNB |
2005 |
Hidden Naive Bayes |
unary, binary, nominal |
binary, nominal |
|
NaiveBayes |
1995 |
Naive Bayes |
unary, binary, nominal, numeric |
binary, nominal |
1, 3 |
NaiveBayesMultinominal |
1998 |
Multinominal Naive Bayes |
numeric |
binary, nominal |
|
NaiveBayesMultinominalUpdateable |
1998 |
Multinominal Naive Bayes (Updatable) |
numeric |
binary, nominal |
|
NaiveBayesSimple |
1973 |
Simple Naive Bayes |
unary, binary, nominal, numeric,
date
|
binary,
nominal |
|
NaiveBayesUpdateable |
1995 |
Naive Bayes (Updatable) |
unary, binary, nominal, numeric |
binary, nominal |
|
WAODE |
2006 |
Weighted AODE |
unary, binary, nominal |
binary, nominal |
|
Functions |
GaussianProcesses |
1998 |
Gaussian processes for regression without hyperparameter-tuning |
unary, binary, nominal, numeric |
numeric, date |
|
IsotonicRegression |
- |
|
numeric, date |
numeric, date
|
|
LeastMedSq |
1987 |
|
unary, binary, nominal, numeric, date |
numeric,
date |
|
LibLINEAR |
2008 |
|
unary, binary, nominal, numeric, date |
binary,
nominal |
|
LibSVM |
2001, 2005 |
|
unary, binary, nominal, numeric, date |
binary,
nominal |
|
LinearRegression |
- |
Linear Regression |
unary, binary, nominal, numeric,
date
|
numeric, date |
|
Logistic |
1992 |
A multinomial logistic regression model with a ridge estimator. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
1 |
MultilayerPerceptron |
- |
A Classifier that uses backpropagation to classify instances. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric, date,
|
1 |
PaceRegression |
2000, 2002 |
|
unary, binary, numeric |
numeric, date |
|
PLSClassifier |
- |
|
numeric, date |
numeric,
date |
|
RBFNetwork |
- |
|
unary, binary, nominal, numeric |
|
|
SimpleLinearRegression |
- |
Simple Linear Regression |
numeric, date |
numeric, date |
|
SimpleLogistic |
2005 |
Linear Logistic Regression |
unary, binary, nominal, numeric,
date
|
binary, nominal |
5 |
SMO |
1998, 2001 |
|
unary, binary, nominal, numeric |
binary, nominal |
3 |
SMO reg |
1998, 1999 |
Implements the support vector machine for regression |
unary, binary, nominal, numeric |
numeric, date |
|
SPegasos |
2007 |
|
unary, binary, nominal, numeric |
binary |
|
Voted Perceptron |
1998 |
Implementation of the voted perceptron algorithm by Freund and Schapire. |
unary, binary, nominal, numeric,
date
|
binary |
|
Winnow |
1988, 1989 |
|
unary, binary, nominal, |
binary |
|
Lazy |
IB1 |
1991 |
Nearest-neighbour classifier. |
unary, binary, nominal, numeric, date |
binary, nominal |
3 |
IBk |
1991 |
k-nearest neighbours |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
KStar |
1995 |
Instance-based classifier |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
LBR |
2000 |
Lazy Bayesian Rules Classifier. |
unary, binary, nominal |
binary, nominal |
|
LWL |
1996, 2003 |
Locally weighted learning |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
Meta |
AdaBoostM1 |
1996 |
Class for boosting a nominal class classifier. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
AdditiveRegression |
1999 |
Enhances the performance of a regression base classifier. |
unary, binary, nominal, numeric,
date
|
numeric, date |
|
AttributeSelectedClassifier |
- |
Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
Bagging |
1996 |
|
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
3 |
ClassificationViaClustering |
- |
A simple meta-classifier that uses a clusterer for classification. |
unary, binary, nominal, numeric |
binary, nominal |
|
ClassificationViaRegression |
1998 |
Doing classification using regression methods. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
CostSensitiveClassifier |
- |
Makes its base classifier cost-sensitive. |
unary, binary, nominal, numeric,
string, date, relational
|
binary, nominal |
3, 4, 5 |
CVParemeterSelection |
1995 |
Performing parameter selection by cross-validation for any classifier. |
unary, binary, nominal, numeric,
string, date, relational
|
binary,
nominal,
numeric,
date |
|
Dagging |
1997 |
|
unary, binary, nominal, numeric |
binary, nominal |
|
Decorate |
2003, 2004 |
Building diverse ensembles of classifiers by using specially constructed artificial training examples. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
END |
2004, 2005 |
Handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
FilteredClassifier |
- |
Running an arbitrary classifier on data that has been passed through an arbitrary filter. |
unary, binary, nominal, numeric,
string, date, relational
|
binary, nominal |
|
Grading |
2001 |
Implements Grading |
unary, binary, nominal, numeric, string, date, relational |
binary, nominal, numeric, date |
|
GridSearch |
- |
|
numeric, date |
numeric, date |
|
LogitBoost |
1998 |
Performing additive logistic regression. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
MetaCost |
1999 |
|
unary, binary, nominal, numeric, date, string, relational |
binary, nominal |
|
MultiBoosting |
2000 |
Boosting a classifier using the MultiBoosting method. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
MultiClassClassifier |
- |
Handling multi-class datasets with 2-class classifiers. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
MultiScheme |
- |
|
unary, binary, nominal, numeric,
string, date, relational
|
binary,
nominal,
numeric,
date |
|
ClassBalancedND |
2004, 2005 |
Handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
DataNearBalancedND |
2004, 2005 |
Handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure. |
unary, binary, nominal, date |
binary, nominal |
|
ND |
2004, 2005 |
Handling multi-class datasets with 2-class classifiers by building a random tree structure. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
OrdinalClassClassifier |
2001 |
Allows standard classification algorithms to be applied to ordinal class problems. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
RacedIncrementalLogitBoost |
2002 |
Classifier for incremental learning of large datasets by way of racing logit-boosted committees. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
RandomCommittee |
- |
Building an ensemble of randomizable base classifiers |
unary, binary, nominal, numeric,
date
|
|
|
RandomSubSpace |
1998 |
|
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
RegressionByDiscretization |
2009 |
|
unary, binary, nominal, numeric,
date
|
numeric, date |
|
RotationForest |
2006 |
Class for construction a Rotation Forest. |
unary, binary, nominal, date |
binary, nominal |
|
Stacking |
1992 |
Combines several classifiers using the stacking method. |
unary, binary, nominal, numeric,
string, date, relational
|
binary,
nominal,
numeric,
date |
|
StackingC |
2002 |
More efficient version of stacking |
unary, binary, nominal, numeric, date, string, relational |
binary, nominal, numeric, date |
|
ThresholdSelector |
- |
|
unary, binary, nominal, numeric, date |
binary |
|
Vote |
1998, 2004 |
Combining classifiers. |
unary, binary, nominal, numeric,
string, date, relational
|
binary,
nominal,
numeric,
date |
|
MI |
CitationKNN |
2000 |
Modified version of the Citation kNN multi instance classifier. |
unary, binary, nominal, numeric, date, relational |
binary, nominal |
|
MDD |
1998 |
Modified Diverse Density algorithm, with collective assumption. |
unary, binary, nominal, relational |
binary |
|
MIBoost |
1996 |
|
unary, binary, nominal, numeric, date, string, relational |
binary |
|
MIDD |
1998 |
Re-implement the Diverse Density algorithm, changes the testing procedure. |
unary, binary, nominal, relational |
binary |
|
MIEMDD |
2001 |
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. |
unary, binary, nominal, relational |
binary |
|
MILR |
- |
Uses either standard or collective multi-instance assumption, but within linear regression. |
unary, binary, nominal, relational |
binary |
|
MINND |
2001 |
Multiple-Instance Nearest Neighbour with Distribution learner |
unary, binary, nominal, relational |
binary, nominal |
|
MIOptimalBall |
2004 |
|
unary, binary, nominal, relational |
binary |
|
MISMO |
1998, 2001 |
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. |
unary, binary, nominal, numeric, relational |
binary, nominal |
|
MISVM |
2003 |
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). |
unary, binary, nominal, relational |
binary |
|
MIWrapper |
2003 |
A simple Wrapper method for applying standard propositional learners to multi-instance data. |
unary, binary, nominal, numeric, date, string, relational |
binary, nominal |
|
SimpleMI |
- |
Reduces MI data into mono-instance data. |
unary, binary, nominal, numeric, date, string, relational |
binary, nominal |
|
Misc |
HyperPipe |
- |
HyperPipe classifier |
unary, binary, nominal ,numeric, date |
binary, nominal |
|
SerializedClassifier |
- |
A wrapper around a serialized classifier model. |
- |
- |
|
VFI |
1997 |
voting feature intervals. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
Rules |
ConjunctiveRule |
- |
Implements a single conjunctive rule learner that can predict for numeric and nominal class labels. |
unary, binary, nominal, numeric, date |
binary, nominal, numeric, date |
|
DecisionTable |
1995 |
Building and using a simple decision table majority classifier. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
DNTB |
2008 |
Building and using a decision table/naive bayes hybrid classifier. |
unary, binary, nominal, numeric, date |
binary, nominal |
|
JRip |
1995 |
Implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER) |
unary, binary, nominal, numeric,
date
|
binary, nominal |
4, 5 |
M5Rules |
1992, 1997, 1999 |
Generates a decision list for regression problems using separate-and-conquer. |
unary, binary, nominal, numeric,
date
|
numeric, date |
|
NNge |
1995,2002 |
|
unary, binary, nominal, numeric, date |
binary, nominal |
4 |
OneR |
1993 |
1R classifier. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
3, 4 |
PART |
1998 |
PART decision list. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
3 |
Prism |
1987 |
Building and using a PRISM rule set for classification. |
unary, binary, nominal |
binary, nominal |
4 |
Ridor |
- |
An implementation of a RIpple-DOwn Rule learner. |
unary, binary, nominal, numeric, date |
binary, nominal |
4 |
ZeroR |
- |
0-R classifier. |
unary, binary, nominal, numeric,
string, date, relational
|
binary,
nominal,
numeric,
date |
3 |
Trees |
ADTree |
1999 |
Class for generating an alternating decision tree. |
unary, binary, nominal, numeric, date |
binary |
4 |
BFTree |
2000, 2007 |
Class for building a best-first decision tree classifier. |
unary, binary, nominal, numeric |
binary |
|
DecisionStump |
- |
Class for building and using a decision stump. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
|
FT |
2004, 2005 |
Functional trees |
unary, binary, nominal, numeric, date |
binary, nominal |
|
Id3 |
1986 |
Class for constructing an unpruned decision tree based on the ID3 algorithm. |
unary, binary, nominal |
binary, nominal |
|
J48 |
1993 |
Generating a pruned or unpruned C4 |
unary, binary, nominal, numeric,
date
|
binary, nominal |
1, 2, 3, 4, 5 |
J48graft |
1999 |
Class for generating a grafted (pruned or unpruned) C4. |
unary, binary, nominal, numeric, |
binary, nominal |
|
LADTree |
2001 |
Class for generating a multi-class alternating decision tree using the LogitBoost strategy. |
unary, binary, nominal , numeric, date |
binary, nominal |
|
LMT |
2005 |
Logistic Model Trees |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
M5P |
1992, 1997 |
M5 Base |
unary, binary, nominal, numeric,
date
|
numeric, date |
2 |
NBTree |
1996 |
Class for generating a decision tree with naive Bayes classifiers at the leaves. |
unary, binary, nominal, numeric,
date
|
binary, nominal |
|
RandomForest |
2001 |
Class for constructing a forest of random trees. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric |
5 |
RandomTree |
- |
Constructing a tree that considers K randomly chosen attributes at each node. |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric |
4 |
REPTree |
- |
Fast decision tree learner |
unary, binary, nominal, numeric,
date
|
binary,
nominal,
numeric,
date |
4 |
SimpleCart |
1984 |
Class implementing minimal cost-complexity pruning. |
unary, binary, nominal, numeric |
binary, nominal |
4, 5 |
UserClassier |
2001 |
Interactively classify through visual means. |
unary, binary, numeric, date, string, relation |
binary, nominal, numeric, date |
|
|