Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff

Loading...
Thumbnail Image
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Date
2019-12-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
7
3616-3622
Series
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Abstract
Exponentially increasing data volumes, coupled with new modes of analysis have created significant new opportunities for data scientists. However, the stochastic nature of many data science techniques results in tradeoffs between costs and accuracy. For example, machine learning algorithms can be trained iteratively and indefinitely with diminishing returns in terms of accuracy. In this paper we explore the cost-accuracy tradeoff through three representative examples: we vary the number of models in an ensemble, the number of epochs used to train a machine learning model, and the amount of data used to train a machine learning model. We highlight the feasibility and benefits of being able to measure, quantify, and predict cost accuracy tradeoffs by demonstrating the presence and usability of these tradeoffs in two different case studies.
Description
Keywords
Other note
Citation
Baughman , M , Chakubaji , N , Truong , L , Kreics , K , Chard , K & Foster , I 2019 , Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff . in Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 . IEEE , pp. 3616-3622 , IEEE International Conference on Big Data , Los Angeles , California , United States , 09/12/2019 . https://doi.org/10.1109/BigData47090.2019.9006370