Download JAR files tagged by selection with all dependencies

JMockit Coverage is a code coverage tool with several metrics (line, path, data) capable of generating HTML reports. It is designed with ease of use in mind, avoiding the need for complex configuration. Instead, smart (but overridable) defaults are employed, such as the selection of which classes to consider for coverage, and where to find sources files for report generation.

This package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.

JMockit Coverage is a code coverage tool with several metrics (line, path, data) capable of generating HTML reports. It is designed with ease of use in mind, avoiding the need for complex configuration. Instead, smart (but overridable) defaults are employed, such as the selection of which classes to consider for coverage, and where to find sources files for report generation.

JMockit Coverage is a code coverage tool with several metrics (line, path, data) capable of generating HTML reports. It is designed with ease of use in mind, avoiding the need for complex configuration. Instead, smart (but overridable) defaults are employed, such as the selection of which classes to consider for coverage, and where to find sources files for report generation.

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

Feature selection method based on correlation measureand relevance and redundancy analysis. Use in conjunction with an attribute set evaluator (SymmetricalUncertAttributeEval). For more information see: Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003.

<p>Sometimes while programming in NetBeans you want to explore a particular file that you are editing on the file system browser, or maybe launch a command in a terminal to do something with it.</p> <p>This plugins brings to your NetBeans six action, three of them always available and three of them available when the selected node has a file assiociated with it.<br/> In particular:<p><p><em>When the selection has a valid file</em></p> <ul> <li><strong>Open the default OS shell</strong> on the location of the file (or its folder) selected.</li> <li><strong>Open the file system browser</strong> on the location of the file (or its folder) selected.</li> <li><strong>Copy to the clipboard</strong> the path of the file selected.</li> </ul> <p><em>Always enabled:</em></p> <ul> <li><strong>Launch a shell command</strong> (with parameters, customizable on preferences)</li> <li><strong>FileSystem browser on any location</strong> (favorites, customizable on preferences)</li> <li><strong>Open a shell on any location</strong> (favorites, customizable on preferences)</li> </ul>

A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.