DPPred: An Effective Prediction Framework with Concise Discriminative Patterns.

In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data.

DPClass: An Effective but Concise Discriminative Patterns-Based Classification Framework.

Pattern-based classification was originally proposed to improve the accuracy using selected frequent patterns, where many efforts were paid to prune a huge number of non-discriminative frequent patterns. On the other hand, tree-based models have shown strong abilities on many classification tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, we propose a natural and effective way to resolve pattern-based classification by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.

Mining Multi-Aspect Reflection of News Events in Twitter: Discovery, Linking and Presentation.

A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter.

Modeling Truth Existence in Truth Discovery.

When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources.