An Integrated Deep Learning Ensemble Model for Optimized Data Mining Performance

Abstract

In the age of big data, being able to find useful patterns and insights in huge and complicated datasets is essential for making smart decisions. The combination of deep learning and machine learning approaches has greatly improved data mining, which is an important step in finding new information. But individual models often have problems like overfitting, being slow to compute, and not performing consistently across different kinds of data. This work suggests an integrated deep learning ensemble model that combines several algorithms and improves them to improve data mining performance. We looked at the best machine learning algorithms and compared them to develop a strong hybrid framework. We looked at the performance, accuracy, and flexibility of algorithms like AdaBoost, LightGBM, and xgboost across a range of fields. Using this information, a hybrid model was developed by strategically combining these methods with deep learning architectures like Convolutional Neural Networks (CNNs). The goal of this hybridization is to use the best parts of classification, clustering, association rule mining, and graph-based ranking in one ensemble structure. We did a lot of tests on benchmark datasets in healthcare, e-commerce, and social networks. The findings show that the suggested hybrid ensemble model does a better job of classifying data than either deep learning or machine learning models on their own. This is true for accuracy, precision, recall, and F1-score. In particular, the model was 15–20% better at making predictions than single models like Decision Tree or CNN alone. The outcomes clearly indicate that the hybrid model provides enhanced accuracy and efficiency in comparison to base models, validating the advantage of combining statistical learning techniques with deep learning in a well-orchestrated manner. Additionally, the model exhibits scalability and flexibility, making it adaptable to different domains and large-scale datasets. Its modular architecture allows for seamless integration with existing data mining systems, while parallel processing support enables deployment in high-performance computing environments.