CEKA: A Tool for Mining the Wisdom of Crowds
Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; 16(88):2853−2858, 2015.
Abstract
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.
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