— Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner

Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.

Although several effective learning-from-crowd methods have been developed to infer correct labels from noisy crowdsourced labels, a method for post-processed expert validation is still needed. This paper introduces a semi-supervised learning algorithm that is capable of selecting the most informative instances and maximizing the influence of expert labels. Specifically, we have developed a complete uncertainty assessment to facilitate the selection of the most informative instances. The expert labels are then propagated to similar instances via regularized Bayesian inference. Experiments on both real-world and simulated datasets indicate that given a specific accuracy goal (e.g., 95%), our method reduces expert effort from 39% to 60% compared with the state-of-the-art method.

This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.