CEKA: A tool for mining the wisdom of crowds

Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


CEKA is a software package for developers and researchers to mine the wisdom of crowds. It makes the entire knowledge discovery procedure much easier, including analyzing qualities of workers, simulating labeling behaviors, inferring true class labels of instances, filtering and correcting mislabeled instances (noise), building learning models and evaluating them. It integrates a set of state-of-the-art inference algorithms, a set of general noise handling algorithms, and abundant functions for model training and evaluation. CEKA is written in Java with core classes being compatible with the well-known machine learning tool WEKA, which makes the utilization of the functions in WEKA much easier.

Original languageEnglish
Pages (from-to)2853-2858
Number of pages6
JournalJournal of Machine Learning Research
StatePublished - Dec 2015


  • Crowdsourcing
  • Inference
  • Learning from crowds
  • Multiple noisy labeling
  • Noise handling
  • Repeated labeling simulation


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