An Information Theoretic Feature Selection Framework for Big Data under Apache Spark
This work provides a scalable solution for feature selection in big data applications, though it is incremental as it adapts existing methods to a distributed platform.
The paper tackled the challenge of feature selection for high-dimensional big data by parallelizing standard information theoretic methods on Apache Spark, resulting in a distributed framework that outperforms sequential versions in speed and accuracy across real-world datasets.
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in interest as an important tool to identify relevant features on huge datasets --both in number of instances and features--. The purpose of this work is to demonstrate that standard feature selection methods can be parallelized in Big Data platforms like Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of a generic feature selection framework which includes a wide group of well-known Information Theoretic methods. Experimental results on a wide set of real-world datasets show that our distributed framework is capable of dealing with ultra-high dimensional datasets as well as those with a huge number of samples in a short period of time, outperforming the sequential version in all the cases studied.