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scala-tptp-parser is a library for parsing the input languages of the TPTP infrastructure for knowledge representation and reasoning. The package contains a data structure for the abstract syntax tree (AST) of the parsed input as well as the parser for the different language of the TPTP, see http://tptp.org for details. In particular, parser are available for: - THF (TH0/TH1): Monomorphic and polymorphic higher-order logic, - TFF (TF0/TF1): Monomorphic and polymorphic typed first-order logic, including extended TFF (TFX), - FOF: Untyped first-order logic, - TCF: Typed clause-normal form, - CNF: (Untyped) clause-normal form, and - TPI: TPTP Process Instruction language. The parser was initially based on v7.4.0.3 of the TPTP syntax BNF (http://tptp.org/TPTP/SyntaxBNF.html), but is continuously updated to keep track of TPTP language updates.

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

The Jentity framework attempts to provide the Java platform with more powerful building blocks for integrating objects and components. The framework focuses on the challenge of maintaining consistent data or state in a dynamic environment. This is achieved by provide reusable, extensible objects, that can use to construct higher order structures like components, applications and systems. Examples of concrete problems that can be effectively solved by using the Jentity framework, are shared models, distributed models, MVC constructs.

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.