An incremental ensemble learning system for Vietnamese e-commerce product classification
Published in 13th International Conference on Knowledge and Systems Engineering (KSE), 2021
Recommended citation: N. T. N. Linh, V. H. Quan, L. H. Ngan, T. D. Phu and H. -Q. Le, "An incremental ensemble learning system for Vietnamese e-commerce product classification," 2021 13th International Conference on Knowledge and Systems Engineering (KSE), 2021, pp. 1-4, doi: 10.1109/KSE53942.2021.9648642. https://doi.org/10.1109/kse53942.2021.9648642
With the booming of e-commerce platforms, text classification models play an increasingly important role in businesses. Major challenges that businesses would face include dataset imbalance, continuously added data, language specificity and product specificity. In this paper, we propose a scalable incremental machine learning system for industrial-scale deployment in real-world business. The system also includes steps to optimize e-commerce product specifics. The proposal tactics including keyword dictionary mapping, sampling technique and ensemble learning delivered better performance when compared to models without them. Our experiments also showed that minibatch SVM produced good results and might be considerable in a lighter system.
N. T. N. Linh, V. H. Quan, L. H. Ngan, T. D. Phu and H. -Q. Le, “An incremental ensemble learning system for Vietnamese e-commerce product classification,” 2021 13th International Conference on Knowledge and Systems Engineering (KSE) , 2021, pp. 1-4, doi: 10.1109/KSE53942.2021.9648642.