sramirez/Gettting-and-Cleaning-Data

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Created over 9 years ago
Updated over 9 years ago

sramirez/Gettting-and-Cleaning-Data

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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.

fast-mRMR

An improved implementation of the classical feature selection method: minimum Redundancy and Maximum Relevance (mRMR).

spark-MDLP-discretization

Spark implementation of Fayyad's discretizer based on Minimum Description Length Principle (MDLP)

MOAReduction

A library for data reduction in MOA (Massive Online Analysis) platform.

spark-RELIEFFC-fselection

Distributed version of RELIEF-F algorithm for Apache Spark.

spark-IS-streaming

A Nearest Neighbor Classifier for High-Speed Big Data Streams with Instance Selection

spark-experiments

spark-DEMD-discretizer

An Entropy Multivariate Discretizer for data reduction on Spark

EMD-discretizer

EMD is an evolutionary-based discretization algorithm with binary representation which selects the most adequate combination of boundary cut points to create discrete intervals.

flink-infotheoretic-feature-selection

This package contains a generic implementation of greedy Information Theoretic Feature Selection (FS) methods for Apache Flink. 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.