Futuristic Classification with Dynamic Reference Frame Strategy
This addresses a practical issue for businesses needing actionable lead time in predictive analytics, but it appears incremental as it builds on existing state-of-the-art techniques.
The paper tackles the problem of providing a time buffer for predictions in classification tasks, such as churn or fault prediction, by introducing a new concept of reference frame creation to enable organizations to act before events occur.
Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper