Distributed Correlation-Based Feature Selection in Spark
This work addresses the problem of handling large-scale feature selection for big data applications, though it is incremental as it adapts an existing algorithm to a distributed framework.
The authors tackled the challenge of scaling correlation-based feature selection (CFS) for big data by developing Distributed CFS (DiCFS) in Apache Spark, achieving superior time-efficiency and scalability on large datasets while maintaining result quality identical to the non-distributed WEKA version.
CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.