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 , 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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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
If you use the sbt-spark-package plugin, in your sbt build file, add:
spDependencies += "sramirez/spark-RELIEFFC-fselection:0.5.0"
resolvers += "Spark Packages Repo" at "http://dl.bintray.com/spark-packages/maven" libraryDependencies += "sramirez" % "spark-RELIEFFC-fselection" % "0.5.0"
MavenIn 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>