Abstract
The identification of workplaces (establishments) most likely to pay low wages is an essential component of effectively monitoring a minimum wage. The main method utilised in this paper is the Variable Precision Rough Sets (VPRS) model, which constructs a set of decision ‘if... then...’ rules. These rules are easily readable by non-specialists and predict the proportion of low paid employees in an establishment. Through a ‘leave n out’ approach a standard error on the predictive accuracy of the sets of rules is calculated, also the importance of the descriptive characteristics is exposited based on their use. To gauge the effectiveness of the VPRS analysis, comparisons are made to a series of decision tree analyses.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
An, A., Shan, N., Chan, C., Cercone, N., Ziarko, W.: Discovering rules for water demand prediction: An enhanced rough-set approach. Engineering Application and Artificial Intelligence 9 (1996) 645–653.
Beynon, M.: An Investigation of β-reduct Selection within the Variable Precision Rough Sets Model. In: The Second International Conference on Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence Series, Springer-Verlag (2000) 114–122
Beynon, M.: Reducts within the Variable Precision Rough Set Model. A Further Investigation. European Journal of Operational Research 134 (2001) 592–605.
Low Pay Commission: The National Minimum Wage: The Story So Far: Second Report of the Low Pay Commission. Cm 4571, London: HMSO (2000).
McNabb, R., Whitfield, K.: ‘Worth So Appallingly Little’: A Workplace-level of Low pay. British Journal of Industrial Relations 38 (2000) 585–609.
Slowinski, R., Stefanowski, J.: RoughDAS and RoughClass software implementations of the rough sets approach. in Intelligent Decision Support, Applications and Advances of the Rough Sets Theory, ed. R. Slowinski, Kluwer, Academic Publishers, London (1992).
Stefanowski, J.: Classification support based on the rough set theory. Proc. IIASA Workshop on User-Oriented Methodlogy and Techniques of Decision Analysis and Support, Serock (1992).
Quinlan, J.R.: Introduction of Decision trees. Machine Learning 1 (1986) 81–106.
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46 (1993) 39–59.
Zighed, D.A., Rabaseda, S., Rakotomala, S.: FUSINTER: A method for discretisation of continuous attributes, International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems 6 (1998) 307–326.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Beynon, M.J. (2002). The Identification of Low-Paying Workplaces: An Analysis Using the Variable Precision Rough Sets Model. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_70
Download citation
DOI: https://doi.org/10.1007/3-540-45813-1_70
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44274-5
Online ISBN: 978-3-540-45813-5
eBook Packages: Springer Book Archive