An Evolutionary and Comparative Study of Machine Learning, Deep Learning, and Hybrid Systems for Prediction and Classification
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Updated time：2026-07-22 16:09:18 Views：18
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Abstract
In recent years, big data technologies have seen tremendous growth in prediction and classification capabilities in complex domains. Traditional machine learning methods typically rely on early stage feature extraction and work best with structured data, generally producing mediocre results for complex problems. With the tremendous increase in available data and in computing power, deep learning has become a mainstream research topic. In images in particular, the convolutional neural network (CNN) has seen tremendous growth thanks to its ability to automatically extract features and to hierarchically construct representations of complex data. This paper presents a structured and synthesis-driven review of 53 studies on the topic of prediction and classification, and organizes them into the following categories: machine learning, CNN-based, optimization, segmentation, hybrid and emerging approaches. In each category, we do not present an in-depth analysis of each study, instead we identify trends, and compare performance metrics, strengths and weaknesses of the different categories. We also introduce a comparative analysis framework for the different methods to compare them on the basis of several performance metrics, the size of the required data set, their computational complexity, and their interpretability. The results of the review clearly show a transition from traditional machine learning to deep learning and to hybrid models that achieve higher accuracy and robustness than traditional models. However, the results also show several challenges, i.e., the models are data-intensive, not interpretable, and not scalable. This review aims to provide a solid overview of the recent progress in prediction and classification, and to provide future research directions towards developing more efficient, scalable, and interpretable models for a variety of applications.
Keywords
Machine Learning, Deep Learning, Convolutional Neural Networks (CNN), Prediction, Classification, Hybrid Models, Ensemble Learning, Data-driven Systems, Comparative Analysis, Model Evaluation, Scalability, Interpretability
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