Recently, with the rapid developments of the Internet and social networks, there have been
tremendous increase in the amount of complex-structured text resources. These information …

Efficient and interpretable classification of time series is an essential data mining task with
many real-world applications. Recently several dictionary-and shapelet-based time series …

The random forest classifier is widely used in different fields due to its accuracy and
robustness. Since its invention, the random forest algorithm is naturally developed for multi …

Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …

Information of the geographical and spatial type is found in numerous text documents and
constitutes a very challenging target for extraction. Geoparsing applications have been …

Online learning is playing an increasingly important role in education. Massive open online
course (MOOC) platforms are among the most important tools in online learning, and record …

Current decision trees such as C4. 5 and CART are widely used in different fields due to
their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers …

This paper introduces the concept of travel behavior embeddings, a method for re-
representing discrete variables that are typically used in travel demand modeling, such as …

The ability to rapidly identify at scale patients that are similar based on their electronic health
records (EHRs) is fundamental for a number of clinical informatics applications, such as …

Missing values arise routinely in real-world sequential (string) datasets due to:(1) imprecise
data measurements;(2) flexible sequence modeling, such as binding profiles of molecular …