Feature selection in high-dimensional dataset using MapReduce
This provides a scalable solution for feature selection in bioinformatics and network inference, but it is incremental as it adapts an existing method to a distributed framework.
The paper tackled the problem of feature selection in high-dimensional datasets by implementing a distributed MapReduce version of the minimum Redundancy Maximum Relevance algorithm, achieving scalability on datasets with millions of observations or features.
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.