The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led increased interest in automated ML solutions that reduce need for human intervention. One main challenges applying previously unseen problems is algorithm selection – identification high-performing algorithm(s) a given dataset, task, evaluation measure. This study addresses ch...

Identifying subgroups of cancer patients is important as it opens up possibilities for targeted therapeutics. A widely applied approach to group with unsupervised clustering techniques based on molecular data tumor samples. The patient clusters are found be interest if they can associated a clinical outcome variable such the survival patients. However, these variables do not participate in deci...

Huge amount of data are nowadays produced by a large and disparate family sensors, which typically measure multiple variables over time. Such rich information can be profitably organized as multivariate time-series. Collect enough labelled samples to set up supervised analysis for such kind is challenging while reasonable assumption dispose limited background knowledge that injected in the proc...

We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed a st...

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...