9 accepted papers
In crowdsourcing scenarios, to mitigate the impact of noisy labels assigned by non-expert workers, each instance is typically annotated multiple times by different workers. However, repeated annotation can introduce instance- or label-level redundancy, thereby inflating annotation costs. Despite its…
In crowdsourcing scenarios, to mitigate the impact of noisy labels assigned by non-expert workers, each instance is typically annotated multiple times by different workers. However, repeated annotation can introduce instance- or label-level redundancy, thereby inflating annotation costs. Despite its…
Due to its simplicity, effectiveness and robustness, naive Bayes (NB) has continued to be one of the top 10 data mining algorithms. To improve its performance, a large number of improved algorithms have been proposed in the last few decades. However, in addition to Gaussian naive Bayes (GNB), there…
In real-world crowdsourcing scenarios, most workers often annotate a few instances only, which results in a significantly sparse crowdsourced label matrix and subsequently harms the performance of label integration algorithms. Recent work called worker similarity-based label completion (WSLC) has be…
Label completion serves as a preprocessing approach to handling the sparse crowdsourced label matrix problem, significantly boosting the effectiveness of the downstream label aggregation. In recent advances, worker modeling has been proved to be a powerful strategy to further improve the performance…
In recent years, a large number of algorithms for label integration and noise correction have been proposed to infer the unknown true labels of instances in crowdsourcing. They have made great advances in improving the label quality of crowdsourced datasets. However, due to the presence of intractab…
To reduce annotation costs, it is common in crowdsourcing to collect only a few noisy labels from different crowd workers for each instance. However, the limited noisy labels restrict the performance of label integration algorithms in inferring the unknown true label for the instance. Recent works h…
In crowdsourcing scenarios, we can obtain multiple noisy labels from different crowd workers on the Internet for each instance and then infer its unknown true label via a label integration method. However, noisy labels often have a serious negative impact on label integration. In this case, most exi…
In crowdsourcing scenarios, we can obtain multiple noisy labels from different crowd workers for each instance and then infer its integrated label via label integration. To achieve better performance, some recently published label integration methods have attempted to exploit the multiple noisy labe…