JCLEC (Java Class Library for Evolutionary Computation) is a software system for Evolutionary Computation research, developed in the Java programming language. It provides a high-level software framework to do any kind of Evolutionary Algorithm (EA), providing support for genetic algorithms (binary, integer and real encoding), genetic programming (Koza’s style, strongly typed, and grammar based) and evolutionary programming.
KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) 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.
datapro4j is a Java API for processing data extracted from diverse heterogeneous sources (e.g. databases, files with different formats, Internet, etc.) making, for example, migration between data formats pretty easy: the programmer only needs to read values from one given format and then write them to another different one. Further, datapro4j allows the programmer to easily develop their own algorithms on datasets.
MLDA (Multi-Label Dataset Analyzer) is an open source (GPLv3) Java software for data exploration and analysis of multi-label (ML) and multi-view multi-label (MVML) datasets which includes both a GUI tool and a Java API. It provides an easy to use tool for multi-label datasets analysis, including a wide set of characterization metrics, charts for measuring the imbalance and relationship among labels, several methods for data preprocessing and transformation, MVML datasets characterization and allowing to load several datasets simultaneously.
JCLAL (Java Class Library for Active Learning) is a general purpose framework developed in Java for the active learning (AL) research area. JCLAL framework is open source and it is distributed under the GNU General Public License. It is constructed with a high-level software environment, with a strong object oriented design and use of design patterns, which allow to adapt, modify or extend the framework according to developer’s needs.
MDM (Moodle Data Mining) Tool is a framework to extract interesting and previously unknown knowledge hidden in Moodle data by means of different DM techniques. Moreover, given that it can be easily integrated into Moodle as a module for a specific course, any of the DM techniques included in this tool use the same interface than Moodle.