A novel ensemble aggregation method based on deep learning representation.

Nguyen, Truong Thanh; Elyan, Eyad; Dang, Truong; Nguyen, Tien Thanh; Longmuir, Martin

Abstract

We propose a novel ensemble aggregation method by using a deep learning-based representation approach. Specifically, we applied the Cross-Validation procedure on training data with a number of learning algorithms to obtain the predictions for training data called meta-data. A neural network model is trained on this meta-data to generate representations associated with class labels. In our method, the neural network model functions as an encoder, learning the relationship between base classifiers' outputs and mapping meta-data to a representation space. The vectors in the mapped space provide a more accurate representation than traditional methods by reducing the distance of vectors in the same class and increasing the distance in different classes. Our method was compared with four well-known ensemble methods: Decision Template, an ensemble with a MultiLayer Perceptron (MLP)-based combiner, gcForest, and XgBoost. Experiments conducted on 20 UCI datasets demonstrate the outstanding performance of our ensemble aggregation method. The results show that our method achieves better delegation of class label representations, enhancing the final results of classification tasks.

Citation

NGUYEN, T.T., ELYAN, E., DANG, T., NGUYEN, T.T. and LONGMUIR, M. 2025. A novel ensemble aggregation method based on deep learning representation. In Antonacopoulos, A., Chaudhuri, S., Chellappa, R., et al. (eds.) Pattern recognition: proceedings of the 27th International conference on pattern recognition, 01-05 December 2024, Kolkata, India. Lecture notes in computer science, 15324. Cham: Springer [online], pages 31-46. Available from: https://doi.org/10.1007/978-3-031-78383-8_3

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 27th International conference on pattern recognition, 01-05 December 2024, Kolkata, India
• Start Date: Dec 1, 2024
• End Date: Dec 5, 2024
• Acceptance Date: Aug 20, 2024
• Online Publication Date: Dec 2, 2024
• Publication Date: Jan 1, 2025
• Deposit Date: Dec 19, 2024
• Publicly Available Date: Dec 3, 2025
• Print ISSN: 0302-9743
• Electronic ISSN: 1611-3349
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 31-46
• Series Title: Lecture notes in computer science (LNCS)
• Series Number: 15324
• Book Title: Pattern recognition: proceedings of the 27th International conference on pattern recognition, 01-05 December 2024, Kolkata, India
• ISBN: 9783031783821
• DOI: https://doi.org/10.1007/978-3-031-78383-8_3
• Keywords: Ensemble learning; Ensemble combining; Ensemble aggregation; Multilayer perceptron; Ensemble method
• Public URL: https://rgu-repository.worktribe.com/output/2625873