Advanced Hybrid Intelligence Framework for Scalable and Robust Big Data Analytics

The phenomena of data explosion in different areas of life require enhanced methods to performing big data analysis. This paper proposes the Hybrid Intelligent Machine Learning System (HIMLS) which work through the combination of multiple machine learning approaches to improve the efficacy of big data analysis. HIMLS applies the benefits of both supervised and unsupervised learning, integrates deep learning models and ensemble methods to solve the difficulties which are from high-dimension learning and heterogeneous data. As a result of combining feature selection with the process of dimensionality reduction HIMLS moderates the curse of dimensionality which enhances the models’ predictive capability while lowering the computational requirements. It is proved that the system works on actual big data implementation, exhibits a better solution in terms of the accuracy of the model, size, and stability aspects in contrast to the conventional machine learning approaches. This research also analyses the effects that different techniques of hyperparameters optimization select and the involvement of distributed computing systems for speeding up analytics. Thus the study infers HIMLS as a suitable solution to the shortcomings characterized by current big data analytical techniques, giving an efficient method for harnessing big data solutions.