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hotSpot from group nz.ac.waikato.cms.weka (version 1.0.3)

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

Group: nz.ac.waikato.cms.weka Artifact: hotSpot
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447 downloads
Artifact hotSpot
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/hotSpot
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

grading from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements Grading. The base classifiers are "graded". For more information, see A.K. Seewald, J. Fuernkranz: An Evaluation of Grading Classifiers. In: Advances in Intelligent Data Analysis: 4th International Conference, Berlin/Heidelberg/New York/Tokyo, 115-124, 2001.

Group: nz.ac.waikato.cms.weka Artifact: grading
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Artifact grading
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/grading
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

filteredAttributeSelection from group nz.ac.waikato.cms.weka (version 1.0.2)

This package provides two meta attribute selection evaluators that can apply an arbitrary filter to the input data before executing the actual attribute selection scheme. One filters data and then passes it to an attribute evaluator (FilteredAttributeEval), and the other filters data and then passes it to a subset evaluator (FilteredSubsetEval).

Group: nz.ac.waikato.cms.weka Artifact: filteredAttributeSelection
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Artifact filteredAttributeSelection
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/filteredAttributeSelection
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

SVMAttributeEval from group nz.ac.waikato.cms.weka (version 1.0.2)

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

Group: nz.ac.waikato.cms.weka Artifact: SVMAttributeEval
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2 downloads
Artifact SVMAttributeEval
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SVMAttributeEval
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

SPegasos from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

Group: nz.ac.waikato.cms.weka Artifact: SPegasos
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1 downloads
Artifact SPegasos
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SPegasos
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

winnow from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)

Group: nz.ac.waikato.cms.weka Artifact: winnow
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1 downloads
Artifact winnow
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/winnow
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

tertius from group nz.ac.waikato.cms.weka (version 1.0.2)

Finds rules according to confirmation measure (Tertius-type algorithm). For more information see: P. A. Flach, N. Lachiche (1999). Confirmation-Guided Discovery of first-order rules with Tertius. Machine Learning. 42:61-95.

Group: nz.ac.waikato.cms.weka Artifact: tertius
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Artifact tertius
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/tertius
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

tabuAndScatterSearch from group nz.ac.waikato.cms.weka (version 1.0.2)

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

Group: nz.ac.waikato.cms.weka Artifact: tabuAndScatterSearch
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1 downloads
Artifact tabuAndScatterSearch
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/tabuAndScatterSearch
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

rotationForest from group nz.ac.waikato.cms.weka (version 1.0.3)

An ensemble learning method inspired by bagging and random sub-spaces. Trains an ensemble of decision trees on random subspaces of the data, where each subspace has been transformed using principal components analysis.

Group: nz.ac.waikato.cms.weka Artifact: rotationForest
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Artifact rotationForest
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/rotationForest
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

probabilisticSignificanceAE from group nz.ac.waikato.cms.weka (version 1.0.2)

Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function (attribute-classes and classes-attribute association). For more information see: Amir Ahmad, Lipika Dey (2004). A feature selection technique for classificatory analysis.

Group: nz.ac.waikato.cms.weka Artifact: probabilisticSignificanceAE
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0 downloads
Artifact probabilisticSignificanceAE
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/probabilisticSignificanceAE
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!



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