Chun-ang Li

Active learning using hint information

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Combination of feature engineering and ranking models for paper-author identification in KDD Cup 2013

The track 1 problem in KDD Cup 2013 is to discriminate between papers confirmed by the given authors from the other deleted papers. This paper describes the winning solution of team National Taiwan University for track 1 of KDD Cup 2013. First, we conduct the feature engineering to transform the various provided text information into 97 features. Second, we train classification and ranking models using these features. Last, we combine our individual models to boost the performance by using results on the internal validation set and the official Valid set. Some effective post-processing techniques have also been proposed. Our solution achieves 0.98259 MAP score and ranks the first place on the private leaderboard of Test set.