Incremental Outlier Detection Modelling Using Streaming Analytics in
  Finance & Health Care
Ch Priyanka Vivek
In this paper, we had built the online model which are built incrementally by
using online outlier detection algorithms under the streaming environment. We
identified that there is highly necessity to have the streaming models to
tackle the streaming data. The objective of this project is to study and
analyze the importance of streaming models which is applicable in the
real-world environment. In this work, we built various Outlier Detection (OD)
algorithms viz., One class Support Vector Machine (OC-SVM), Isolation Forest
Adaptive Sliding window approach (IForest ASD), Exact Storm, Angle based
outlier detection (ABOD), Local outlier factor (LOF), KitNet, KNN ASD methods.
The effectiveness and validity of the above-built models on various finance
problems such as credit card fraud detection, churn prediction, ethereum fraud
prediction. Further, we also analyzed the performance of the models on the
health care prediction problems such as heart stroke prediction, diabetes
prediction and heart stroke prediction problems. As per the results and dataset
it shows that it performs well for the highly imbalanced datasets that means
there is a majority of negative class and minority will be the positive class.
Among all the models, the ensemble model strategy IForest ASD model performed
better in most of the cases standing in the top 3 models in almost all of the
cases.