A Novel Online and Non-Parametric Approach for Drift Detection in Big Data | Zendy

Moinak Bhaduri | Zendy; Justin Zhan | Zendy; Carter Chiu | Zendy; Felix Zhan | Zendy

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A Novel Online and Non-Parametric Approach for Drift Detection in Big Data

Author(s) -

Moinak Bhaduri,

Justin Zhan,

Carter Chiu,

Felix Zhan

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2735378

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A sizable amount of current literature on online drift detection tools thrive on unrealistic parametric strictures such as normality or on non-parametric methods whose power performance is questionable. Using minimal realistic assumptions such as unimodality, we have strived to proffer an alternative, through a novel application of Bernstein's inequality. Simulations from such parametric densities as Beta and Logitnormal as well as real-data analyses demonstrate this new method's superiority over similar techniques relying on bounds, such as Hoeffding's. Improvements are apparent in terms of higher power, efficient sample sizes, and sensitivity to parameter values.

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