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Group: Waikato CMS Weka

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1. Weka Dev225 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 21, 2020

2. Weka Stable65 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 21, 2020
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. 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

8. 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
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. 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

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

15. 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
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

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. MEKA1 usages

nz.ac.waikato.cms.weka » meka

Artifactory auto generated POM
Last Release on May 15, 2013

19. Normalize1 usages

nz.ac.waikato.cms.weka » normalizeGPL

An instance filter that normalize instances considering only numeric attributes and ignoring class index
Last Release on Apr 26, 2012

20. SMOTE1 usages

nz.ac.waikato.cms.weka » SMOTEGPL

Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). The original dataset must fit entirely in memory. The amount of SMOTE and number of nearest neighbors may be specified. For more information, see Nitesh V. Chawla et. al. (2002). Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 16:321-357.
Last Release on Apr 3, 2013