KEEL – Java tool to assess evolutionary algorithms for data mining

Last Updated on August 14, 2023

KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source Java software tool that can be used for a large number of different knowledge data discovery tasks.

KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones.

KEEL has been designed with a two-fold goal: research and education.

It’s written in the Java programming language.

Features include:

• Large collection of evolutionary algorithms for predicting models, preprocessing methods (evolutionary feature and instance selection among others) and postprocessing procedures (evolutionary tuning of fuzzy rules). It also presents many state-of-the-art methods for different areas of data mining such as decision trees, fuzzy rule based systems or crisp rule learning.
• 100 data preprocessing algorithms proposed in the specialized literature: data transformation, discretization, instance and feature selection, noise filtering and so forth.
• Incorporates a statistical library to analyze the results of the algorithms.
• Comprises a set of statistical tests for analyzing the suitability of the results and for performing parametric and nonparametric comparisons among the algorithms. It provides an user-friendly interface, oriented to the analysis of algorithms.
• Create experiments containing multiple datasets and algorithms to obtain results. Experiments are independently script-generated from the user interface for an offline run in any machine that supports a Java Virtual Machine.
• Contains a Knowledge Extraction Algorithms Library, remarking the incorporation of multiple evolutionary learning algorithms, together with classical learning approaches. The main employment lines are:
  • Different evolutionary rule learning models have been implemented
  • Fuzzy rule learning models with a good trade-off between accuracy and interpretability.
  • Evolution and pruning in neural networks, product unit neural networks, and radial base models.
  • Genetic Programming: Evolutionary algorithms that use tree representations for extracting knowledge.
  • Algorithms for extracting descriptive rules based on patterns subgroup discovery have been integrated.
  • Data reduction (training set selection, feature selection and discretization). EAs for data reduction have been included.
• Cross-platform support – runs on all major platforms.

Website: www.keel.es
Support: Documentation
Developer: KEEL Developers
License: GNU General Public License v3.0

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