Citizen science and human computation involves working with multiple, untrusted decision makers. We demonstrate how Bayesian Classifier Combination outperforms a naive Bayes method when classifying documents using unreliable crowdsourced labels. We also present methods for screening workers and selecting informative documents to label. Finally, we explain how the Bayesian Classifier Combination...

Citizen science and human computation involves working with multiple, untrusted decision makers, whose performance depends on training, rewards, ability and interest. We first present methods for screening workers and selecting informative objects to label. We then demonstrate Bayesian Classifier Combination as an effective method for classifying documents using unreliable crowdsourced labels. ...

In this paper, we introduce a new approach to the classification of streaming data based on bootstrap aggregation (bagging). The proposed approach creates an ensemble model by using ID3 classifier, naïve Bayesian classifier, and k-Nearest-Neighbor classifier for a learning scheme where each classifier gives the weighted prediction. ID3, naïve Bayesian, and k-NearestNeighbor classifiers are very...

In this paper, we apply popular Bayesian techniques on support vector classifier. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristics of natural normalization in the likelihood function, and describe a Bayesian framework in stationary Gaussian stochastic processes. In this framework, Bayesian inference is used to implement model ...

This paper presents a novel approach for online multi-strokes composite sketchy shape recognition based on Bayesian Networks. By means of the definition of a double-level Bayesian networks, a classifier is designed to model the intrinsic temporal orders among the strokes effectively, where a sketchy shape is modeled with the relationships not only between a stroke and its neighbouring strokes, ...

In this paper, naive Bayesian and C4.5 Decision Tree Classifiers(DTC) are successively applied in materials informatics to classify the engineering materials into different classes for the selection of materials that suit the input design specifications. Here, the classifiers are analyzed individually and their performance evaluation is analyzed with confusion matrix predictive parameters and s...

In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and semantic dependency relations. An initial investigation is then done using a Naive Bayesian Classifier and some handcrafted rules. The results show that the algorithms and proposed approa...

This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, Email messages consist of a set of fields with predefined semantics and a number of variable length free-text fields. While most work on classification either concentrates on structured data or free text, the work in this paper deals with both of them. To p...