Crowdsourcing services provide a fast and cheap way to obtain substantial labeled data by employing crowd workers on the Internet. In crowdsourcing learning, two-stage methods have been widely used, which first infer the integrated label for each instance and then build a learning model using instances with their integrated labels. However, existing two-stage methods mainly focus on how to infer more accurate integrated labels, after that, most of them directly regard the integrated labels as class labels to build a learning model, which loses the detailed worker labeling information in multiple noisy labels and thus results in sub-optimal model accuracy. To solve this problem, in this study, we take the multiple noisy labels of each instance as its attribute value vector to construct another view in addition to the original attribute view, and propose a novel two-stage method called dual-view learning from crowds (DVLFC). In DVLFC, we first pick out workers with sufficient number of labels and augment the multiple noisy label set for each instance, then we build a supervised learning model in each view and at last we fuse their class-membership probabilities to get the final classification result. Extensive experiments on both real-world and artificial crowdsourced datasets prove the effectiveness of DVLFC.

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. Numerous enhancements have been proposed to weaken its attribute conditional independence assumption. However, all of them only focus on the raw attribute view, which is hard to reflect all the data characteristics in real-world applications. To portray data characteristics more comprehensively, in this study, we construct two label views from the raw attributes and propose a novel model called multi-view attribute weighted naive Bayes (MAWNB). In MAWNB, we first build multiple super-parent one-dependence estimators (SPODEs) as well as random trees (RTs), then we utilize each of them to classify each training instance in turn and use all their predicted class labels to construct two label views. Next, to avoid attribute redundancy, we optimize the weight of each attribute value for each class by minimizing the negative conditional log-likelihood (CLL) in each view. Finally, the estimated class-membership probabilities by three views are fused to predict the class label for each test instance. Extensive experiments show that MAWNB significantly outperforms NB and all the other existing state-of-the-art competitors.