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Group: Waikato CMS Weka

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1. Weka Dev215 usages

nz.ac.waikato.cms.weka » weka-devGPL

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.
Last Release on Dec 20, 2019

2. Weka Stable60 usages

nz.ac.waikato.cms.weka » weka-stableGPL

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other breaking updates.
Last Release on Dec 20, 2019
Group Waikato CMS Weka Thirdparty

4. LibSVM7 usages

nz.ac.waikato.cms.weka » LibSVMGPL

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier). LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier. LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.)
Last Release on Nov 20, 2016

5. RotationForest5 usages

nz.ac.waikato.cms.weka » rotationForestGPL

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.
Last Release on Apr 26, 2012
This package provides two classes - one for evaluating the merit of individual attributes using a classifier (ClassifierAttributeEval), and second for evaluating the merit of subsets of attributes using a classifier (ClassifierSubsetEval). Both invoke a user-specified classifier to perform the evaluation, either under cross-validation or on the training data.
Last Release on Oct 16, 2014

7. SPegasos3 usages

nz.ac.waikato.cms.weka » SPegasosGPL

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 ...
Last Release on Apr 26, 2012

8. PaceRegression3 usages

nz.ac.waikato.cms.weka » paceRegressionGPL

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is ...
Last Release on Apr 26, 2012
This package provides four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch. See: David E. Goldberg (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley. Mark Hall, Geoffrey Holmes (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6):1437-1447.
Last Release on Apr 27, 2014

10. DistributedWekaBase3 usages

nz.ac.waikato.cms.weka » distributedWekaBaseGPL

This package provides generic configuration class and distributed map/reduce style tasks for Weka
Last Release on Feb 27, 2018

11. PartialLeastSquares3 usages

nz.ac.waikato.cms.weka » partialLeastSquaresGPL

This package contains a filter for computing partial least squares and transforming the input data into the PLS space. It also contains a classifier for performing PLS regression.
Last Release on Jan 11, 2018
Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large ...
Last Release on Apr 26, 2012
Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.
Last Release on Apr 27, 2014

14. MultiInstanceLearning2 usages

nz.ac.waikato.cms.weka » multiInstanceLearningGPL

A collection of multi-instance learning classifiers. Includes the Citation KNN method, several variants of the diverse density method, support vector machines for multi-instance learning, simple wrappers for applying standard propositional learners to multi-instance data, decision tree and rule learners, and some other methods.
Last Release on Feb 21, 2017
Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets. In: Proceedings of the 5th International Conferenceon Discovery Science, 153-164, 2002.
Last Release on Apr 26, 2012

16. MultiInstanceFilters2 usages

nz.ac.waikato.cms.weka » multiInstanceFiltersGPL

A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. ...
Last Release on Nov 23, 2018

17. PredictiveApriori2 usages

nz.ac.waikato.cms.weka » predictiveAprioriGPL

Class implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.
Last Release on Aug 4, 2014

18. XMeans1 usages

nz.ac.waikato.cms.weka » XMeansGPL

Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, ...
Last Release on Feb 28, 2018

19. DiscriminantAnalysis1 usages

nz.ac.waikato.cms.weka » discriminantAnalysisGPL

Currently only contains Fisher's Linear Discriminant Analysis.
Last Release on Oct 28, 2018

20. Decorate1 usages

nz.ac.waikato.cms.weka » decorateGPL

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles ...
Last Release on Apr 26, 2012