An Enhanced Deep Learning Approach for High-Dimensional and Complex Datasets

Abstract

The increasing availability of extensive datasets with many features in contemporary scenarios imposes serious limitations on traditional machine learning and deep learning models due to such challenges as feature redundancy, noise sensitivity, and scalability restrictions. Therefore, this paper presents a powerful deep learning approach that, through its adaptive feature learning, integrated regularization, and efficient optimization strategies, is capable of overcoming these difficulties. The proposed method performs the feature selection and focus on the most informative features automatically, while the redundant and irrelevant ones are eliminated. As a result, the representational quality and generalization ability can be significantly enhanced. Extensive results obtained on benchmark as well as real-world datasets hardly any cases have been proposed to prove not only the superiority of the newly developed method over the baseline machine learning methods and even the latest deep learning models in terms of accuracy and robustness but also its application potential in the practice. Further, the component removal experiments provide support for the significance of the different parts of the design and the statistical significance tests justify the reliability of the performance gains observed. Also, the suggested technique results in improved computational performance, and as a result, it could be implemented in scenarios associated with a large amount of data and high-dimensional data processing. To sum up, their results highlight that the proposed model provides an effective and scalable solution to tackle complicated datasets.