nz.ac.waikato.cms.weka » 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.
更新时间: 2014-08-05 06:53nz.ac.waikato.cms.weka » classAssociationRules
Class association rules algorithms (including an implementation of the CBA algorithm). For more information see: W. Li, J. Han, J.Pei: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In ICDM'01:369-376,2001. B. Liu, W. Hsu, Y. Ma: Integrating Classification and Association Rule Mining. In KDD'98:80-86,1998.
更新时间: 2014-07-29 11:10nz.ac.waikato.cms.weka » fuzzyUnorderedRuleInduction
FURIA: Fuzzy Unordered Rule Induction Algorithm. For details please see: Jens Christian Huehn, Eyke Huellermeier (2009). FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery.
更新时间: 2014-07-29 10:17nz.ac.waikato.cms.weka » extraTrees
Package for generating a single Extra-Tree. Use with the RandomCommittee meta classifier to generate an Extra-Trees forest for classification or regression. This classifier requires all predictors to be numeric. Missing values are not allowed. Instance weights are taken into account. For more information, see Pierre Geurts, Damien Ernst, Louis Wehenkel (2006). Extremely randomized trees. Machine Learning. 63(1):3-42.
更新时间: 2014-04-30 09:14Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.
更新时间: 2014-04-30 08:53nz.ac.waikato.cms.weka » denormalize
An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.
更新时间: 2014-04-29 18:33nz.ac.waikato.cms.weka » dagging
This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.
更新时间: 2014-04-29 16:32nz.ac.waikato.cms.weka » costSensitiveAttributeSelection
This package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.
更新时间: 2014-04-29 16:22nz.ac.waikato.cms.weka » conjunctiveRule
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weig
更新时间: 2014-04-29 15:56nz.ac.waikato.cms.weka » complementNaiveBayes
Class for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.
更新时间: 2014-04-29 12:37nz.ac.waikato.cms.weka » clojureClassifier
Wrapper classifier for classifiers written in the Clojure language.
更新时间: 2014-04-28 19:21nz.ac.waikato.cms.weka » chiSquaredAttributeEval
Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.
更新时间: 2014-04-27 17:04nz.ac.waikato.cms.weka » bestFirstTree
Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.
更新时间: 2014-04-27 16:54nz.ac.waikato.cms.weka » attributeSelectionSearchMethods
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.
更新时间: 2014-04-27 16:43nz.ac.waikato.cms.weka » alternatingDecisionTrees
Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
更新时间: 2014-04-27 16:28| 仓库 | 个数 |
| Central | 592045 |