Title: Outlier Detection by Consistent Data Selection Method,
Abstract: Often the challenge associated with tasks like fraud and spam detection[1] is
the lack of all likely patterns needed to train suitable supervised learning
models. In order to overcome this limitation, such tasks are attempted as
outlier or anomaly detection tasks. We also hypothesize that out- liers have
behavioral patterns that change over time. Limited data and continuously
changing patterns makes learning significantly difficult. In this work we are
proposing an approach that detects outliers in large data sets by relying on
data points that are consistent. The primary contribution of this work is that
it will quickly help retrieve samples for both consistent and non-outlier data
sets and is also mindful of new outlier patterns. No prior knowledge of each
set is required to extract the samples. The method consists of two phases, in
the first phase, consistent data points (non- outliers) are retrieved by an
ensemble method of unsupervised clustering techniques and in the second phase a
one class classifier trained on the consistent data point set is ap- plied on
the remaining sample set to identify the outliers. The approach is tested on
three publicly available data sets and the performance scores are competitive.