Decision Tree Design for Classification in Crowdsourcing Systems
Authors:
- 1 October 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 859-863
Abstract
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems. Considering that workers are unreliable and they perform the tests with errors, we study the construction of decision trees so as to minimize the probability of mis-classification. By exploiting the connection between probability of mis-classification and entropy at each level of the decision tree, we propose two algorithms for decision tree design. Furthermore, the worker assignment problem is studied when workers can be assigned to different tests of the decision tree to provide a trade-off between classification cost and resulting error performance. Numerical results are presented for illustration.Keywords
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This publication has 11 references indexed in Scilit:
- Classification And Regression Trees2017
- Multi-Object Classification via Crowdsourcing With a Reject OptionIEEE Transactions on Signal Processing, 2016
- Task assignment optimization in knowledge-intensive crowdsourcingThe VLDB Journal, 2015
- Reliable Crowdsourcing for Multi-Class Labeling Using Coding TheoryIEEE Journal of Selected Topics in Signal Processing, 2014
- CDASProceedings of the VLDB Endowment, 2012
- Amazon's Mechanical TurkPerspectives on Psychological Science, 2011
- Quality management on Amazon Mechanical Turk2010
- A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification AlgorithmsMachine Learning, 2000
- Induction of decision treesMachine Learning, 1986
- Application of information theory to the construction of efficient decision treesIEEE Transactions on Information Theory, 1982