Abstract
In this chapter, we introduce the concepts of semi-heuristic data partitioning, and present a proposed multi-granularity framework for semi-heuristic data partitioning. We also discuss the advantages of the proposed framework in terms of dealing with class imbalance and the sample representativeness issue, from granular computing perspectives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Esfahani, M.S., and E.R. Dougherty. 2014. Effect of separate sampling on classification accuracy. Bioinformatics 30 (2): 242–250.
K. Lang, E. Liberty, and K. Shmakov. 2016. Stratified sampling meets machine learning. In Proceedings of the 33rd International Conference on Machine Learning. New York: JMLR.org, 2320–2329.
C.-E. S\(\ddot{{\rm {a}}}\)rndal B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer.
H. Liu and M. Cocea. Semi-random partitioning of data into training and test sets in granular computing context. Granular Computing, 2 (4) 2017.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Liu, H., Cocea, M. (2018). Multi-granularity Semi-random Data Partitioning. In: Granular Computing Based Machine Learning. Studies in Big Data, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-70058-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-70058-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70057-1
Online ISBN: 978-3-319-70058-8
eBook Packages: EngineeringEngineering (R0)