An Ensemble Model for 2D-Data Classification Based on Classical & Deep Learning Classifier

Ensemble learning is one of the most studied topics in classification domain, it is proven that ensemble learning is effective for classification tasks with multiple labels. Nevertheless, achieving accurate predictions for data with varying dimensions and characteristics remains a formidable task. Enhancing the generalization capabilities of ensemble classifiers poses a significant challenge in the field of ensemble learning. To address this issue and improve accuracy, we propose a novel ensemble method. In our method, a combination of deep learning classifier (i.e., CNN, Bi-LSTM) and classical classifiers (e.g., KNN, SVM, Naive Bayes, Dtree) is constructed and then the optimal weighting parameters for the base classifier are assigned. We enhance the model's robustness by employing lasso regularization to reduce data dimensions. The results on 10 folds show that our method can significantly enhance the model's generalization performance and precision in classification tasks. Our model consistently outperforms traditional and existing ensemble methods across 18 publicly available datasets, as evidenced by accuracy and the Friedman test.