Decision Tree Design for Classification in Crowdsourcing Systems
By Baocheng Geng, Qunwei Li, Pramod K. Varshney. 2018
10 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Carrie J. Cai
- Michael Terry
- Tongshuang Sherry Wu
- Ellen Jiang
- Chitta Baral
- Swaroop Mishra
- Mihir Parmar
- Percy Liang
In Computer Science Applications
In Evaluation & Benchmarks
Citations in the corpus
- PromptChainer: Chaining Large Language Model Prompts through Visual Programming (2022, background): “Their chain structures reflect different high-level patterns: (1) P1 and P2 built chains with parallel logic branches, similar to decision trees [7].”