1. Spark Infotheoretic Feature Selection
This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods. The implementation is based on the common theoretic framework presented by Gavin Brown. Implementations of mRMR, InfoGain, JMI and other commonly used FS filters are provided.
2. Fast Mrmr
An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).
1-2 of 2 user projects