Search Results for "c4.5 algorithm in weka"

Weka genetic algorithm filter plugin to generate synthetic instances. This Weka Plugin implementation uses a Genetic Algorithm to create new synthetic instances to solve the imbalanced dataset problem. See my master thesis available for download, for further details.

This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule induction. ...

...Gannu includes some graphical interfaces for scientific purposes. When using Gannu please cite: *Jiménez, F. V., Gelbukh, A. F. & Sidorov, G. (2013). Simple Window Selection Strategies for the Simplified Lesk Algorithm for Word Sense Disambiguation.. In F. Castro, A. F. Gelbukh & M. González (eds.), MICAI (1) (pp. 217-227), : Springer. ISBN: 978-3-642-45113-3 The zip file contains Gannu jar, source, API documentation and necessary resources for performing research. Gannu uses the following projects: Weka, JExcel API, Stanford POS Tagger and WordNet. ...

weka outlier is an implementation of outlier detection algorithms for WEKA. CODB (Class Outliers: Distance-Based) Algorithm is the first algorithm developed using WEKA framework.

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

A discretization algorithm based on the Minimum Description Length. Implemented as a filter according to the standards and interfaces of WEKA, the Java API for Machine Learning. More Info: http://bruno-wp.blogspot.com/search/label/Software

KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

Weka++ is a collection of machine learning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.