  Classification is a fundamental problem in machine learning and data mining.
During the past decades, numerous classification methods have been presented
based on different principles. However, most existing classifiers cast the
classification problem as an optimization problem and do not address the issue
of statistical significance. In this paper, we formulate the binary
classification problem as a two-sample testing problem. More precisely, our
classification model is a generic framework that is composed of two steps. In
the first step, the distance between the test instance and each training
instance is calculated to derive two distance sets. In the second step, the
two-sample test is performed under the null hypothesis that the two sets of
distances are drawn from the same cumulative distribution. After these two
steps, we have two p-values for each test instance and the test instance is
assigned to the class associated with the smaller p-value. Essentially, the
presented classification method can be regarded as an instance-based classifier
based on hypothesis testing. The experimental results on 40 real data sets show
that our method is able to achieve the same level performance as the
state-of-the-art classifiers and has significantly better performance than
existing testing-based classifiers. Furthermore, we can handle outlying
instances and control the false discovery rate of test instances assigned to
each class under the same framework.

Machine Learning;Machine Learning