Spark Rough Hypercuboid Approach for Scalable Feature Selection
- 14 September 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. PP (10414347), 1
- https://doi.org/10.1109/tkde.2021.3112520
Abstract
Feature selection refers to choose an optimal non-redundant feature subset with minimal degradation of learning performance and maximal avoidance of data overfitting. The appearance of large data explosion leads to the sequential execution of algorithms are extremely time-consuming, which necessitates the scalable parallelization of algorithms by efficiently exploiting the distributed computational capabilities. In this paper, we present parallel feature selection algorithms underpinned by a rough hypercuboid approach in order to scale for the growing data volumes. Metrics in terms of rough hypercuboid are highly suitable to parallel distributed processing, and fits well with the Apache Spark cluster computing paradigm. Two data parallelism strategies, namely, vertical partitioning and horizontal partitioning, are implemented respectively to decompose the data into concurrent iterative computing streams. Experimental results on representative datasets show that our algorithms significantly faster than its original sequential counterpart while guaranteeing the quality of the results. Furthermore, the proposed algorithms are perfectly capable of exploiting the distributed-memory clusters to accomplish the computation task that fails on a single node due to the memory constraints. Parallel scalability and extensibility analysis have confirmed that our parallelization extends well to process massive amount of data and can scales well with the increase of computational nodes.Keywords
Funding Information
- National Major Science and Technology Project of China (2018AAA0100201)
- National Natural Science Foundation of China (62076171, 61573292, 61976182)
This publication has 23 references indexed in Scilit:
- A parallel rough set based dependency calculation method for efficient feature selectionApplied Soft Computing, 2018
- Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduceSimulation Modelling Practice and Theory, 2016
- Scalable Semi-Supervised Learning by Efficient Anchor Graph RegularizationIEEE Transactions on Knowledge and Data Engineering, 2016
- On quick attribute reduction in decision-theoretic rough set modelsInformation Sciences, 2016
- Towards scalable fuzzy–rough feature selectionInformation Sciences, 2015
- The two sides of the theory of rough setsKnowledge-Based Systems, 2015
- Hierarchical attribute reduction algorithms for big data using MapReduceKnowledge-Based Systems, 2015
- A Parallel Matrix-Based Method for Computing Approximations in Incomplete Information SystemsIEEE Transactions on Knowledge and Data Engineering, 2014
- A Rough Set-Based Method for Updating Decision Rules on Attribute Values’ Coarsening and RefiningIEEE Transactions on Knowledge and Data Engineering, 2014
- Positive approximation: An accelerator for attribute reduction in rough set theoryArtificial Intelligence, 2010