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NoHR (Nova Hybrid Reasoner) is a plug-in for the ontology editor Protégé that allows its users to query knowledge bases composed of both an Ontology in OWL 2 EL or QL and a set of Reasoning Rules. Using a top-down reasoning approach, which means that only the part of the ontology and rules that is relevant for the query is actually evaluated, NoHR respectively combines the capabilities of ELK for OWL 2 EL and a dedicated direct translation for OWL 2 QL with the rule engine XSB Prolog to deliver very fast interactive response times. NoHR is the first hybrid reasoner of its kind for Protégé. NoHR is also distributed as an API.

This module contains a classpath scanner, and bytecode reader. It is useful in any java runtime environment, and allows java code to parse bytecode into a meta structure of classes, annotations, methods and fields. This meta structure is used throughout xapi to allow us to extend java structures in a cross-platform compliant way (exposing services and service objects so you can hack support for whatever runtime you need). It is especially useful for fast runtime injection, and as an extensible structure during codegen (which exposes mutable interfaces for our meta structure)

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).

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.

A bank decides using various criteria whether a customer wil receive a loan. An insurer uses specific information to determine which insurance product is most suitable for its customer. A municipality makes decisions based on yet other information, to determine whether it will grant permits or if a citizen is entitled to a public transport pass. Decision trees are frequently used in order to make such decisions or recommendations. Albero is a new generation decision tree solution. It is simple, fast, and flexible. Further, Albero is easily adaptable and can integrate easily into any environment.