nz.ac.waikato.cms.weka » DilcaDistance
This package implements the parameter free version of the DILCA distance. This approach allows to learn value-to-value distances between each pair of values for each attribute of the dataset. The distance between two values is computed indirectly based on the their distribution w.r.t. a set of related attributes (the context) carefully chosen.
更新时间: 2014-04-26 16:07nz.ac.waikato.cms.weka » userClassifier
Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit. For more information see: Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.
更新时间: 2014-04-25 15:56nz.ac.waikato.cms.weka » thresholdSelector
A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier. The midpoint threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).
更新时间: 2014-04-25 15:06nz.ac.waikato.cms.weka » J48graft
Class for generating a grafted (pruned or unpruned) C4.5 decision tree. For more information, see Geoff Webb: Decision Tree Grafting From the All-Tests-But-One Partition.
更新时间: 2014-04-25 09:50nz.ac.waikato.cms.weka » localOutlierFactor
A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable. For more information, see: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.
更新时间: 2013-07-23 15:43nz.ac.waikato.cms.weka » supervisedAttributeScaling
Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .
更新时间: 2013-06-27 16:11nz.ac.waikato.cms.weka » kernelLogisticRegression
This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determi
更新时间: 2013-06-26 11:42nz.ac.waikato.cms.weka » oneClassClassifier
Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any class
更新时间: 2013-05-14 16:13nz.ac.waikato.cms.weka » 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.
更新时间: 2013-04-04 03:36nz.ac.waikato.cms.weka » metaCost
This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improveme
更新时间: 2013-02-06 13:01nz.ac.waikato.cms.weka » averagedOneDependenceEstimators
AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
更新时间: 2012-07-20 16:25nz.ac.waikato.cms.weka » WekaODF
WekaODF adds support to directory read from and write to spreadsheets in ODF (Open Document Format for Office Applications, ISO/IEC 26300:2006) format. ODF is used by the OpenOffice.org suite, for instance. WekaODF uses jOpenDocument (http://www.jOpenDocument.org, GPL) in order to read/write ODF spreadsheets.
更新时间: 2012-05-13 16:57nz.ac.waikato.cms.weka » phmm4weka
This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.
更新时间: 2012-04-28 05:57nz.ac.waikato.cms.weka » alternatingModelTrees
Grows an alternating model tree by minimising squared error. For more information see "Eibe Frank, Michael Mayo, Stefan Kramer: Alternating Model Trees. In: Proceedings of the ACM Symposium on Applied Computing, Data Mining Track, 2015".
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » associationRulesVisualizer
visualization component for displaying association rules that uses a modified version of the Association Rules Viewer from DESS IAGL of Lille. Requires Java 3D to be installed.
更新时间: 1970-01-01 08:00| 仓库 | 个数 |
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