  Classification is one of the most popular and widely used supervised learning
tasks, which categorizes objects into predefined classes based on known
knowledge. Classification has been an important research topic in machine
learning and data mining. Different classification methods have been proposed
and applied to deal with various real-world problems. Unlike unsupervised
learning such as clustering, a classifier is typically trained with labeled
data before being used to make prediction, and usually achieves higher accuracy
than unsupervised one.
  In this paper, we first define classification and then review several
representative methods. After that, we study in details the application of
classification to a critical problem in drug discovery, i.e., drug-target
prediction, due to the challenges in predicting possible interactions between
drugs and targets.
