Group: nz.ac.waikato.cms.weka - All Dependencies
weka-dev · 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.
LibSVM · 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.)
rotationForest · 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.
multiInstanceFilters · 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. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.
classifierBasedAttributeSelection · 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.
multiInstanceLearning · 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.
fastCorrBasedFS · Feature selection method based on correlation measureand relevance and redundancy analysis. Use in conjunction with an attribute set evaluator (SymmetricalUncertAttributeEval). For more information see: Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003.
prefuseTree · A visualization component for displaying tree structures from those schemes that can output trees (e.g. decision tree learners, Cobweb clusterer etc.). This component is available from the popup menu in the Explorer's classify and cluster panels. The component uses the prefuse visualization library.
optics_dbScan · The OPTICS and DBScan clustering algorithms. Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, 226-231, 1996; Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD International Conference on Management of Data, 49-60, 1999.
predictiveApriori · 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.
XMeans · 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, 2000.
decorate · 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 Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.
SMOTE · 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.
prefuseGraph · A visualization component for displaying tree structures from those schemes that can output graphs (e.g. bayes nets). This component is available from the popup menu in the Explorer's classify. The component uses the prefuse visualization library.
bayesianLogisticRegression · Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization.
niftiLoader · Package for loading a directory containing MRI data in NIfTI format. The directory to be loaded must contain as many subdirectories as there are classes of MRI data. Each subdirectory name will be used as the class label for the corresponding .nii files in that subdirectory. (This is the same strategy as the one used by WEKA's TextDirectoryLoader.) Currently, the package only reads volume information for the first time slot from each .nii file. The readDoubleVol(short ttt) method from the Nifti1Dataset class (http://niftilib.sourceforge.net/java_api_html/Nifti1Dataset.html) is used to read the data for each volume into a sparse WEKA instance (with ttt=0). For an LxMxN volume (the dimensions must be the same for each .nii file in the directory!), the order of values in the generated instance is [(z_1, y_1, x_1), ..., (z_1, y_1, x_L), (z_1, y_2, x_1), ..., (z_1, y_M, x_L), (z_2, y_1, x_1), ..., (z_N, y_M, x_L)]. If the volume is an image, then only x and y coordinates are used.
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