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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 improvement when only those iterations containing each training instance are used in reclassifying that instance).

This library aims to provide a central access point to the services exposed on the ESB without forcing each part of the system to know the actual location of the ESB and the names of the exposed endpoints. For this sake it allows to map service URI (ie referring to service in webLab taxonomy) to the actual exposed URL on the ESB within the context of a specific project. The library and the configuration should be added in portal lib in order to be central and available to any portlets. The mapping is done in a simple spring config file "webLabClient.xml" which must be accessible in JAVA classpath.

Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported. sIB assign for each instance the cluster that have the minimum cost/distance to the instance. The trade-off beta is set to infinite so 1/beta is zero. For more information, see: Noam Slonim, Nir Friedman, Naftali Tishby: Unsupervised document classification using sequential information maximization. In: Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval, 129-136, 2002.

An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions. For more information about Ripple-Down Rules, see: Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst. 5(3):211-228.

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 Scale Attribute Selection Using Wrappers. Freiburg, Germany.

Kanbanery (https://kanbanery.com) is a simple but powerful Agile project management system, to be precise it focuses around the idea of Kanban, a somewhat near idea to SCRUM but with less strict rules. The heart of each Kanban flow is the Kanban board, IceBox and Archive - there are all easy accessible via this API. Janbanery wraps around the RESTful API delivered by Kanbanery while adding some more features like mass operations or filtering of results. In the end, it's very easy and pleasant to implement your own Kanbanery client be it on the desktop, mobile (android) or as for example Gradle / SBT script to take full advantage of kanbanery's features.

Kanbanery (https://kanbanery.com) is a simple but powerful Agile project management system, to be precise it focuses around the idea of Kanban, a somewhat near idea to SCRUM but with less strict rules. The heart of each Kanban flow is the Kanban board, IceBox and Archive - there are all easy accessible via this API. Janbanery wraps around the RESTful API delivered by Kanbanery while adding some more features like mass operations or filtering of results. In the end, it's very easy and pleasant to implement your own Kanbanery client be it on the desktop, mobile (android) or as for example Gradle / SBT script to take full advantage of kanbanery's features.