Projects

Divmod Reverend

Reverend is a general purpose Bayesian classifier written in Python, named after Rev. Thomas Bayes.

Sorted Pulse Data Library (SPDLib)

A library of tools and API (C++ and Python) for the storage and processing of large 3D laser scanning (LiDAR, ALS, TLS) datasets using a pulse based spatially indexed file format (SPD) which support for both discrete return and full waveform datasets.

OTB Applications

This package includes a set of applications for remote sensing image processing such as orthorectification, classification, object extraction... Few of these applications come with a full GUI interface. Applications are based on the Orfeo Toolbox (OTB) library. More complex applications are now ... [More] developed in the integrated application: Monteverdi. OTB Applications is distributed under a free software licence CeCILL (similar to GPL) to encourage contribution from users and to promote reproducible research. [Less]

randomforest-matlab

This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests' - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port ... [More] by Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package. I especially am grateful for all the help i got from Andy Liaw. This project would not have been possible if not for the previous code by Andy Liaw, Matthew Wiener, Leo Brieman, Adele Cutler. The wiki has short articles on using rfImpute to input in missing values and basic installation procedures. 1-march-2010 Bug: Note the inputs to the package are in double. So make sure you are [Less]

mlpy - Machine Learning Python

Machine Learning PY is a high-performance Python/NumPy based package for Machine Learning.

MALLET

MALLET (A Machine Learning for Language Toolkit) is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text

GNAT NER

GNAT is a library and web service capable of performing gene entity NER and normalization of biomedical articles. Mentions of genes and proteins in the articles are linked to to Entrez Gene identifiers. GNAT is available both for local download (suitable for large-scale processing) and as a web ... [More] service (suitable for more limited processing or testing). A combination of local and remote processing is also available, where CPU-heavy operations are performed locally and memory-intensive operations are performed remotely (this is suitable for large-scale processing where a large amount of memory is not available). GNAT uses LINNAEUS (Gerner et al., 2010) for species detection and BANNER (Leaman et al., 2008) in one part of its false positive filtering process. [Less]

fast-random-forest

What is FastRandomForest?FastRandomForest is a re-implementation of the Random Forest classifier (RF) for the Weka environment that brings speed and memory use improvements over the original Weka RF. Speed gains depend on many factors, but a 5-10x increase over Weka 3-6-1 on a quad core computer ... [More] is not uncommon, along with a 1.5x reduction in memory use. For detailed tests of speed and classification accuracy, as well as description of optimizations in the code, please refer to the FastRandomForest wiki at http://code.google.com/p/fast-random-forest/w or email the author at fran.supek\AT\irb.hr. Unrelated to the FastRF project, an MPI-enabled version of the Random Forest algorithm written in Fortran 90 is available from http://parf.googlecode.com. LicenseThis program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. Using from own Java codeJust add FastRandomForest.jar to your Java VM classpath by using the -cp switch, or by changing project dependencies in NetBeans/Eclipse/whatever IDE you use. Then use hr.irb.fastRandomForest.FastRandomForest as you would use any other classifier, see instructions at the WekaWiki: http://weka.sourceforge.net/wiki/index.php/Use_Weka_in_your_Java_code Using from Weka Explorer or Experimenter (versions 3.7.0, 3.6.1, 3.5.7 or earlier)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” If "cp=" doesn't exist, search for "cmd_default=" and add after "#wekajar#;". 2. You need to extract the “GenericPropertiesCreator.props” file from your weka.jar (jar files are in fact ordinary zip archives, the GenericPropertiesCreator.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-6") 4. Under the # Lists the Classifiers-Packages I want to choose fromheading, add the line hr.irb.fastRandomForestDo not forget to add a comma and a backslash to the previous line. 5. Use the “FastRandomForest” class is in the hr.irb.fastRandomForest package in the "Classify" tab. The other three classes cannot be used directly. Using from Weka Explorer or Experimenter (versions 3.5.8 or 3.6.0 only)1. Add the FastRandomForest.jar to your Java classpath when starting Weka. This is normally done by editing the line beginning with “cp=” in “RunWeka.ini” 2. Extract the “GenericObjectEditor.props” file from weka.jar (jar files are in fact ordinary zip archives, the GenericObjectEditor.props is under /weka/gui). 3. Place the file you've just extracted into the directory where you have installed Weka (on Windows this is commonly "C:\Program Files\Weka-3-5") 4. Find the # Lists the Classifiers I want to choose fromheading and scroll far down to the end of the block (first empty line), then add a line: hr.irb.fastRandomForest.FastRandomForestDo not forget to append a comma and a backslash to the previous line. 5. The “FastRandomForest” class is in the "hr.irb.fastRandomForest" package in the "Classify" tab. Enjoy. [Less]

Multi-label Extension to Weka

Multi-label methods using the Weka and MOA machine learning frameworks

mulan

We developed a package of Java classes for Multi-label classification, called Mulan. The package contain source files and compiled classes of several problem transformation methods for multilabel classification, an evaluation framework that computes several multilabel classification evaluation ... [More] measures and a statistics class providing. The software is distributed under the GNU GPL licence. The package requires Java v1.5 or better and Weka v3.5.5. [Less]