Search Results for "decision tree excel"

...The latest version includes the following toxicity prediction modules: -Cramer rules (oral toxicity) -Toxicity mode of action via Verhaar scheme -Skin irritation and Eye irritation prediction -Benigni / Bossa rulebase for mutagenicity and carcinogenicity prediction -START biodegradation and persistence prediction -Skin sensitisation reactivity domain -Kroes TTC Decision tree -SMARTCyp - Cytochrome P450-Mediated Drug Metabolism and metabolites prediction -Structure Alerts for the in vivo micronucleus assay in rodents (ISSMIC) -Structural Alerts for Functional Group Identification (ISSFUNC) -Structural alerts associated with covalent protein binding and DNA binding. - Ames mutagenicity Toxtree provides a plugin framework to incorporate different approaches to the estimation. ...

A Java-based powerful disk usage utility. It outputs a table and a tree (both can be sorted). TinyScan is able to export the result to Excel, Access, MySQL and it can also scan remote computers. It is free and open source.

MYRA is a collection of Ant Colony Optimization (ACO) algorithms for the data mining classification task. It includes popular rule induction and decision tree induction algorithms. The algorithms are ready to be used from the command line or can be easily called from your own Java code. They are build using a modular architecture, so they can be easily extended to incorporate different procedures and/or use different parameter values. This project is now hosted at: https://github.com/febo/myra

DT4J is a Java implementation of Decision Table based rules engine. It supports excel based and an editor based Decision Table authoring. It integrates with Spring expressions (SpEL) to express conditions and actions. Put together it's a power rules engine.

A tool for analysis of Bayesian Belief Networks/Decision Networks in Genie 2.0 (.xdsl) format. Developed as a part of the HELICOPTER project (http://www.helicopter-aal.eu).

Adaboost extensions for cost-sentive classification CSExtension 1 CSExtension 2 CSExtension 3 CSExtension 4 CSExtension 5 AdaCost Boost CostBoost Uboost CostUBoost AdaBoostM1 Implementation of all the listed algorithms of the cluster "cost-sensitive classification". They are the meta algorithms which requires base algorithms e.g. Decision Tree Moreover, Voting criteria is also required e.g. Minimum expected cost criteria Input also requires to load an arff file and a cost matrix (sample arff and cost files are uploaded for users' reference) This extension uses weka for classification and generates the classification model along with confusion matrix. ...

OpenRules - a full-scale open source Business Decision Management System. Enables business analysts & software developers to work together creating and maintaining complex Rules-based decsion support applications. Supported from WWW.OPENRULES.COM

JBoost is a simple, robust system for classification. JBoost contains implementations of several boosting algorithms in an alternating decision tree framework. In addition, JBoost provides extensible software for adding more learning algorithms.

Compiler of a 0+ order rule system. From a ruleset using attribute value formalism a decision tree is build and java/C/C++ execution code will be generated.

Avenzoar is a one-year exploration of renal cell carcinoma morphology and its related single nucleotide polymorphisms (SNPa) as a method of automating diagnosis of cancer by using a computer-aided decision tree controlled by analytical statistics.

Highly reusable and extensible Decision-Tree (Max-Gain) framework comprising of comprehensive input-processing and display functionality. Handles nominal, linear, continuous data. For preliminary description, refer - http://sushain.com/blog/archives/

The Decision Tree Learning algorithm ID3 extended with pre-pruning for WEKA, the free open-source Java API for Machine Learning. It achieves better accuracy than WEKA's ID3, which lacks pre-pruning.Info: http://bruno-wp.blogspot.com/search/label/Softwar

DTreeJungle provides educational applets to teach the concepts of decision trees for regular pattern recognition.