A Hybrid Statistical–Machine Learning Framework for Robust Sensor Data Analytics in Noisy Environments

Abstract

Intelligent systems that run on sensors in real-world settings are vulnerable to extreme levels
of noise, missed measurements, outliers and non-stationary interference, and these adversely
impact the stability and credibility of data-driven analytics. Although classical methods of
statistical signal processing offer mathematically practical solutions to noise suppression and
uncertainty measurements, they can be relatively weak due to the limiting assumptions of
the noise distribution and the system dynamics. Conversely, machine learning models have
a high capacity of nonlinear representation but highly sensitive to noise problems, data
corruption, and change in distribution. To overcome these limitations, this paper provides
a powerful hybrid statistical-machine learning model that is to be used with a high level of
reliability in sensor data analytics in highly noisy settings. The suggested method applies
probabilistic noise characterization, adaptive statistical filtering, and uncertainty-aware
feature extraction in conjunction with resilient machine learning models to the problem to
obtain better resilience to various noise scenarios. Statistical preprocessing helps in reducing
noise, outliers, incomplete data, and uncertainty-sensitive aspects influence adaptive learning
and other decision-making aspects. The learning layer uses robust and ensemble based
models which are noise regularised to stay stable and generalise in case of non-stationary
conditions. The framework is assessed in a systematic manner in synthetic datasets and also
through real sensor datasets in varied fields of application with controlled noise profiles.
It is proven that the suggested hybrid method proves to be always superior to standalone
statistical and machine learning techniques in the predictive accuracy, robustness, and
generalisation within high-noise and mixed-noise conditions. The findings emphasise the
usefulness of statistical rigour combined with adaptive learning synergistically, thus making
the proposed framework an effective solution to other intelligent systems based on sensors
with a high degree of scalability and domain-agnosticity.