spark-infotheoretic-feature-selection | package contains a generic implementation

spark-infotheoretic-feature-selection is a Scala library typically used in Big Data, Spark applications. spark-infotheoretic-feature-selection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

The present framework implements Feature Selection (FS) on Spark for its application on Big Data problems. This package contains a generic implementation of greedy Information Theoretic Feature Selection methods. The implementation is based on the common theoretic framework presented in [1]. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided. In addition, the framework can be extended with other criteria provided by the user as long as the process complies with the framework proposed in [1]. Please cite as: S. Ramírez-Gallego; H. Mouriño-Talín; D. Martínez-Rego; V. Bolón-Canedo; J. M. Benítez; A. Alonso-Betanzos; F. Herrera, "An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, pp.1-13, doi: 10.1109/TSMC.2017.2670926 URL: