This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.

Project Samples

Project Activity

See All Activity >

License

GNU General Public License version 3.0 (GPLv3)

Follow ExSTraCS

ExSTraCS Web Site

Other Useful Business Software
Migrate to innovate with Red Hat Enterprise Linux on Azure Icon
Migrate to innovate with Red Hat Enterprise Linux on Azure

Streamline your IT modernization journey with a holistic environment running Red Hat Enterprise Linux on Azure.

With Red Hat Enterprise Linux on Azure, businesses can confidently modernize their IT environment, knowing they don’t have to compromise on security, scalability, reliability, and ease of management. Securely accelerate innovation and unlock a competitive edge with enterprise-grade modern cloud infrastructure.
Rate This Project
Login To Rate This Project

User Ratings

★★★★★
★★★★
★★★
★★
0
1
0
0
0
ease 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 3 / 5
features 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 5 / 5
design 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 4 / 5
support 1 of 5 2 of 5 3 of 5 4 of 5 5 of 5 3 / 5

User Reviews

Be the first to post a review of ExSTraCS!

Additional Project Details

Intended Audience

Financial and Insurance Industry, Science/Research, Education

Programming Language

Python

Related Categories

Python Bio-Informatics Software, Python Machine Learning Software

Registered

2014-06-21