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A light weight JAVA performance tool. Its sampling technique allows 24x7 performance monitoring in production with negligible and predicted overhead.

Jcrfsuite is a Java interface for crfsuite, a fast implementation of Conditional Random Fields, using SWIG and class injection technique

Evaluates the worth of an attribute by computing the Probabilistic Significance as a two-way function (attribute-classes and classes-attribute association). For more information see: Amir Ahmad, Lipika Dey (2004). A feature selection technique for classificatory analysis.

Zappos-json is a Java library that can be used to convert plain old Java objects (POJO) into their JSON representation, and vice versa. It uses code generation technique to improve the performance by inlining serialization-related code into a generated class file.

Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). The original dataset must fit entirely in memory. The amount of SMOTE and number of nearest neighbors may be specified. For more information, see Nitesh V. Chawla et. al. (2002). Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 16:321-357.

eDelfoi is a research program based on Delphi expert method. It is developed in co-operation with Future Research Center of Turku School of Economics. Delphi technique is used for bringing values, new viewpoints and ideas as a foundation for planning and decision making, i.e. making qualitative research. The program can also be used for making a simple, Survey-type of query. The newest version of the program is called eDelfoi.

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

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.

Sirix is a temporal storage system effectively and efficiently storing snapshots of time varying (currently semi-structured) data taking full advantage of flash based drives as for instance SSDs. We not only provide several ways of navigating the tree-structure of a single revision, we also support navigation in time. Furthermore we provide a novel storage technique called sliding snapshot to circumvent intermitant full dump snapshots to fast track their in-memory reconstruction and thus we avoiding write peaks and having to read long chains of page fragments/increments/deltas. Sirix uses copy-on-write (COW) as well as an append-only storage making it an ideal candidate for flash based drives while not dropping support for erstwhile disks.