Description:

LogReg-Crowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of:

• Rodrigues, F., Pereira, F., and Ribeiro, B. Learning from multiple annotators: distinguishing good from random labelers. Pattern Recognition Letters, pp. 1428–1436, 2013.

• Raykar, V., Yu, S., Zhao, L., Valadez, G., Florin, C., Bogoni, L., and Moy, L. Learning from Crowds. Journal of Machine Learning Research, pp. 1297–1322, 2010.

• Dawid, A. P. and Skene, A. M. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society. Series C, 28(1):20–28, 1979.

All implementations are able to handle multi-class problems and do not require repeated labelling (i.e. annotators do not have to provide labels for the entire dataset). The code was though for interpretability and it is well commented, so that it can be very easy to use (kindly see the file “demo.jl”). At the same, the Julia language provides it with a great perfomance, specially when compared to other scientific languages such as MATLAB or Python/Numpy, without compromising its high-level and interpretability.

Project homepage: https://github.com/fmpr/LogReg-Crowds

Changes to previous version:

Initial Announcement on mloss.org. Added GitHub page.

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Csv

Tags: Em, Logistic Regression, Crowdsourcing, Latent Variable Model

Archive: download here

Other available revisons

Version	Changelog	Date
1.0	Initial Announcement on mloss.org. Added GitHub page.	January 16, 2017, 18:10:57
v1.0	Initial Announcement on mloss.org. Added GitHub page.	January 16, 2017, 18:07:32
1.0	Initial Announcement on mloss.org. Added GitHub page.	October 30, 2014, 19:10:23

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