spark-RELIEFFC-fselection (homepage)

Distributed version of RELIEF-F algorithm for Apache Spark.

@sramirez / (0)

The present algorithm (called BELIEF) implements Feature Weighting (FW) on Spark for its application on Big Data problems. This repository contains an improved implementation of RELIEF-F algorithm [1], which has been extended with a cheap but effective feature redundancy elimination technique. BELIEF leverages distance computations computed in prior steps to estimate inter-feature redundancy relationships at virtually no cost. BELIEF is also highly scalable to different sample sizes, from hundreds of samples to thousands.


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How to

Include this package in your Spark Applications using:

spark-shell, pyspark, or spark-submit

> $SPARK_HOME/bin/spark-shell --packages sramirez:spark-RELIEFFC-fselection:0.5.0

sbt

If you use the sbt-spark-package plugin, in your sbt build file, add:

spDependencies += "sramirez/spark-RELIEFFC-fselection:0.5.0"

Otherwise,

resolvers += "Spark Packages Repo" at "http://dl.bintray.com/spark-packages/maven"

libraryDependencies += "sramirez" % "spark-RELIEFFC-fselection" % "0.5.0"

Maven

In your pom.xml, add:
<dependencies>
  <!-- list of dependencies -->
  <dependency>
    <groupId>sramirez</groupId>
    <artifactId>spark-RELIEFFC-fselection</artifactId>
    <version>0.5.0</version>
  </dependency>
</dependencies>
<repositories>
  <!-- list of other repositories -->
  <repository>
    <id>SparkPackagesRepo</id>
    <url>http://dl.bintray.com/spark-packages/maven</url>
  </repository>
</repositories>

Releases

Version: 0.5.0 ( 01a74b | zip | jar ) / Date: 2018-04-09 / License: Apache-2.0 / Scala version: 2.11