Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …

Traditional learning from crowdsourced labeled data consists of two stages: inferring true
labels for instances from their multiple noisy labels and building a learning model using …

Crowdsourcing provides a practical approach for obtaining annotated data to train
supervised learning models. However, since the crowd annotators may have different …

Due to low quality of crowdsourced labelers, the integrated label of each example is usually
inferred from its multiple noisy labels provided by different labelers. This paper proposes a …

Crowd work provides solutions to complex problems effectively, efficiently, and at low cost.
Previous research showed that feedback, particularly correctness feedback can help crowd …

Crowdsourcing allows large-scale and flexible invocation of human input for data gathering
and analysis, which introduces a new paradigm of data mining process. Traditional data …

With the increasing demand for large amount of labeled data, crowdsourcing has been used
in many large-scale data mining applications. However, most existing works in …

Crowdsourcing is widely adopted in many domains as a popular paradigm to outsource
work to individuals. In the machine learning community, crowdsourcing is commonly used as …

With crowdsourcing systems, labels can be obtained with low cost, which facilitates the
creation of training sets for prediction model learning. However, the labels obtained from …

Crowdsourcing has recently become popular among machine learning researchers and
social scientists as an effective way to collect large-scale experimental data from distributed …