  We propose thresholding as an approach to deal with class imbalance. We
define the concept of thresholding as a process of determining a decision
boundary in the presence of a tunable parameter. The threshold is the maximum
value of this tunable parameter where the conditions of a certain decision are
satisfied. We show that thresholding is applicable not only for linear
classifiers but also for non-linear classifiers. We show that this is the
implicit assumption for many approaches to deal with class imbalance in linear
classifiers. We then extend this paradigm beyond linear classification and show
how non-linear classification can be dealt with under this umbrella framework
of thresholding. The proposed method can be used for outlier detection in many
real-life scenarios like in manufacturing. In advanced manufacturing units,
where the manufacturing process has matured over time, the number of instances
(or parts) of the product that need to be rejected (based on a strict regime of
quality tests) becomes relatively rare and are defined as outliers. How to
detect these rare parts or outliers beforehand? How to detect combination of
conditions leading to these outliers? These are the questions motivating our
research. This paper focuses on prediction of outliers and conditions leading
to outliers using classification. We address the problem of outlier detection
using classification. The classes are good parts (those passing the quality
tests) and bad parts (those failing the quality tests and can be considered as
outliers). The rarity of outliers transforms this problem into a
class-imbalanced classification problem.
