**123**
results

**123**
results

- Go to itemClass for generating an alternating decision tree. The basic algorithm is based on: Freund, Y., Mason, L.: The alternating decisi…0
- Go to itemAODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that h…0
- Go to itemAODEsr augments AODE with Subsumption Resolution.AODEsr detects specializations between two attribute values at classification ti…0
- Go to itemClass for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dra…0
- Go to itemMeta classifier that enhances the performance of a regression base classifier. Each iteration fits a model to the residuals left …0
- Go to itemClass implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules…0
- Go to itemDimensionality of training and test data is reduced by attribute selection before being passed on to a classifier. (based on WEKA…0
- Go to itemClass for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. F…0
- Go to itemClass for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more i…0
- Go to itemBayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier. Provides …0
- Go to itemImplements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D…0
- Go to itemYiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings o…0
- Go to itemClass for performing parameter selection by cross-validation for any classifier. For more information, see: R. Kohavi (1995). Wra…0
- Go to itemA meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. …0
- Go to itemA simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, …0
- Go to itemClass for doing classification using regression methods. Class is binarized and one regression model is built for each class valu…0
- Go to itemClass implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.…0
- Go to itemClass for building and using a Complement class Naive Bayes classifier. For more information see, Jason D. Rennie, Lawrence Shih,…0
- Go to itemThis class implements a single conjunctive rule learner* that can predict for numeric and nominal class labels. A rule consists o…0
- Go to itemA metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweightin…0

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