{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T03:33:53Z","timestamp":1783481633529,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Hierarchical classification, which organizes items into structured categories and subcategories, has emerged as a powerful solution for handling large and complex datasets. However, traditional flat classification approaches often overlook the hierarchical dependencies between classes, leading to suboptimal predictions and limited interpretability. This paper addresses these challenges by proposing a novel integration of tree-based models with hierarchical-aware split criteria through adjusted entropy calculations. The proposed method calculates entropy at multiple hierarchical levels, ensuring that the model respects the taxonomic structure during training. This approach aligns statistical optimization with class semantic relationships, enabling more accurate and coherent predictions. Experiments conducted on real-world datasets structured according to the GS1 Global Product Classification (GPC) system demonstrate the effectiveness of our method. The proposed model was applied using tree-based ensemble methods combined with the newly developed hierarchy-aware metric Penalized Information Gain (PIG). PIG was implemented with level-wise entropy adjustments, assigning greater weight to higher hierarchical levels to maintain the taxonomic structure. The model was trained and evaluated on two real-world datasets based on the GS1 Global Product Classification (GPC) system. The final dataset included approximately 30,000 product descriptions spanning four hierarchical levels. An 80-20 train\u2013test split was used, with model hyperparameters optimized through 5-fold cross-validation and Bayesian search. The experimental results showed a 12.7% improvement in classification accuracy at the lowest hierarchy level compared to traditional flat classification methods, with significant gains in datasets featuring highly imbalanced class distributions and deep hierarchies. The proposed approach also increased the F1 score by 12.6%. Despite these promising results, challenges remain in scaling the model for very large datasets and handling classes with limited training samples. Future research will focus on integrating neural networks with hierarchy-aware metrics, enhancing data augmentation to address class imbalance, and developing real-time classification systems for practical use in industries such as retail, logistics, and healthcare.<\/jats:p>","DOI":"10.3390\/bdcc9030065","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:59:52Z","timestamp":1741683592000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing Hierarchical Classification in Tree-Based Models Using Level-Wise Entropy Adjustment"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0628-8218","authenticated-orcid":false,"given":"Olga","family":"Narushynska","sequence":"first","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7214-5108","authenticated-orcid":false,"given":"Anastasiya","family":"Doroshenko","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5974-9310","authenticated-orcid":false,"given":"Vasyl","family":"Teslyuk","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4544-4612","authenticated-orcid":false,"given":"Volodymyr","family":"Antoniv","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9978-7072","authenticated-orcid":false,"given":"Maksym","family":"Arzubov","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Junardi, W., and Khodra, M.L. (2020, January 19\u201320). Automatic Multi-Label Classification for GDP Economic-Phenomenon News. Proceedings of the 2020 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia.","DOI":"10.1109\/ICISS50791.2020.9307579"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"72730","DOI":"10.1109\/ACCESS.2024.3400693","article-title":"A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification","volume":"12","author":"Cotacallapa","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"Uddin, M.A., Aryal, S., Bouadjenek, M.R., Al-Hawawreh, M., and Talukder, M.A. (2024). Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis. arXiv."},{"key":"ref_4","unstructured":"Cao, Y.-K., Wei, Z.-Y., Tang, Y.-J., and Jin, C.-K. (2023, January 12\u201314). Hierarchical Label Text Classification Method with Deep-Level Label-Assisted Classification. Proceedings of the 2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS), Xiangtan, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chang, C.-M., Mishra, S.D., and Igarashi, T. (2019, January 14\u201318). A Hierarchical Task Assignment for Manual Image Labeling. Proceedings of the 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL\/HCC), Memphis, TN, USA.","DOI":"10.1109\/VLHCC.2019.8818828"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fan, Q., and Qiu, C. (2023, January 24\u201326). Hierarchical Multi-Label Text Classification Method Based on Multi-Level Decoupling. Proceedings of the 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), Guangzhou, China.","DOI":"10.1109\/NNICE58320.2023.10105736"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rejeb, A., Keogh, J.G., Martindale, W., Dooley, D., Smart, E., Simske, S., Wamba, S.F., Breslin, J.G., Bandara, K.Y., and Thakur, S. (2022). Charting Past, Present, and Future Research in the Semantic Web and Interoperability. Future Internet, 14.","DOI":"10.3390\/fi14060161"},{"key":"ref_8","unstructured":"(2024, December 17). Global Product Classification (GPC). Available online: https:\/\/www.gs1.org\/standards\/gpc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sep\u00falveda-Rojas, J.P., Aravena, S., and Carrasco, R. (2024). Increasing Efficiency in Furniture Remanufacturing with AHP and the SECI Model. Sustainability, 16.","DOI":"10.3390\/su162310339"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhu, X., Wang, M., Wang, X., and Zou, M. (2024, January 10\u201312). Text Classification for Social Governance: A Novel Strategy with Adaptive Reward Mechanisms. Proceedings of the 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China.","DOI":"10.1109\/CISCE62493.2024.10653108"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Chai, B., Song, B., and Zhao, J. (2020, January 10\u201313). A Hierarchical Fine-Tuning Based Approach for Multi-Label Text Classification. Proceedings of the 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China.","DOI":"10.1109\/ICCCBDA49378.2020.9095668"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, H., Song, C., Han, R., and Hu, T. (2021, January 17\u201319). Research on Hierarchical Clustering Undersampling and Random Forest Fusion Classification Method. Proceedings of the 2021 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China.","DOI":"10.1109\/PIC53636.2021.9687089"},{"key":"ref_13","first-page":"277","article-title":"Evaluating the Impact of GINI Index and Information Gain on Classification Using Decision Tree Classifier Algorithm*","volume":"11","author":"Tangirala","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1109\/TKDE.2019.2959991","article-title":"Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification","volume":"33","author":"Peng","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","unstructured":"Rao, S.X., Egger, P.H., and Zhang, C. (2023). Hierarchical Classification of Research Fields in the \u201cWeb of Science\u201d Using Deep Learning 2024. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s10618-010-0175-9","article-title":"A Survey of Hierarchical Classification across Different Application Domains","volume":"22","author":"Silla","year":"2011","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_17","unstructured":"Nowozin, S. (July, January 26). Improved Information Gain Estimates for Decision Tree Induction. Proceedings of the 29th International Conference on Machine Learning (ICML\u201912), Edinburgh, Scotland."},{"key":"ref_18","unstructured":"Naik, A., and Rangwala, H. (2016). Filter Based Taxonomy Modification for Improving Hierarchical Classification. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cai, X., Xiao, M., Ning, Z., and Zhou, Y. (2023, January 1\u20134). Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-Based Data Augmentation. Proceedings of the 2023 IEEE International Conference on Data Mining (ICDM), Shanghai, China.","DOI":"10.1109\/ICDM58522.2023.00107"},{"key":"ref_20","unstructured":"Heinsen, F.A. (2022). Tree Methods for Hierarchical Classification in Parallel. arXiv."},{"key":"ref_21","unstructured":"Asadi, A.R. (2022). An Entropy-Based Model for Hierarchical Learning 2023. arXiv."},{"key":"ref_22","first-page":"124","article-title":"Text Coherence Analysis Based on Misspelling Oblivious Word Embeddings and Deep Neural Network","volume":"12","author":"Wadud","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_23","first-page":"98","article-title":"Application of Global Optimization Methods to Increase the Accuracy of Classification in the Data Mining Tasks","volume":"2353","author":"Doroshenko","year":"2019","journal-title":"Comput. Model. Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108130","DOI":"10.1016\/j.ress.2021.108130","article-title":"A Hierarchical Bayesian-Based Model for Hazard Analysis of Climate Effect on Failures of Railway Turnout Components","volume":"218","author":"Dindar","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Busson, A.J.G., Rocha, R., Gaio, R., Miceli, R., Pereira, I., Moraes, D.d.S., Colcher, S., Veiga, A., Rizzi, B., and Evangelista, F. (2023, January 6\u201311). Hierarchical Classification of Financial Transactions Through Context-Fusion of Transformer-Based Embeddings and Taxonomy-Aware Attention Layer. Proceedings of the Anais do II Brazilian Workshop on Artificial Intelligence in Finance (BWAIF 2023), Jo\u00e3o Pessoa, Para\u00edba.","DOI":"10.5753\/bwaif.2023.229322"},{"key":"ref_26","unstructured":"Rambow, O., Wanner, L., Apidianaki, M., Al-Khalifa, H., Eugenio, B.D., and Schockaert, S. (2025, January 19\u201324). TEXT-CAKE: Challenging Language Models on Local Text Coherence. Proceedings of the 31st International Conference on Computational Linguistics, Abu Dhabi, United Arab Emirates."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shu, H., Cao, L., Xu, Z., and Liu, K. (2009, January 15\u201317). The Research of Multidimensional Information Decision Mining Based on Information Entropy. Proceedings of the 2009 International Forum on Information Technology and Applications, Chengdu, China.","DOI":"10.1109\/IFITA.2009.559"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, J., Zhang, P., Qin, S., and Gong, J. (2015, January 3\u20134). A Method of the Feature Selection in Hierarchical Text Classification Based on the Category Discrimination and Position Information. Proceedings of the 2015 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration, Wuhan, China.","DOI":"10.1109\/ICIICII.2015.116"},{"key":"ref_29","unstructured":"Mutsaddi, A., Jamkhande, A., Thakre, A., and Haribhakta, Y. (2025). BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Williams, L., Anthi, E., and Burnap, P. (2024). Comparing Hierarchical Approaches to Enhance Supervised Emotive Text Classification. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8040038"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zangari, A., Marcuzzo, M., Rizzo, M., Giudice, L., Albarelli, A., and Gasparetto, A. (2024). Hierarchical Text Classification and Its Foundations: A Review of Current Research. Electronics, 13.","DOI":"10.3390\/electronics13071199"},{"key":"ref_32","first-page":"137","article-title":"Text Categorization with Support Vector Machines: Learning with Many Relevant Features","volume":"Volume 1398","author":"Rouveirol","year":"1998","journal-title":"Machine Learning: ECML-98"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016, January 12\u201317). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Basu, S., Bilenko, M., and Mooney, R.J. (2004, January 22\u201325). A Probabilistic Framework for Semi-Supervised Clustering. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA.","DOI":"10.1145\/1014052.1014062"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sebastiani, F. (2006). Classification of Text, Automatic. Encyclopedia of Language & Linguistics, Elsevier.","DOI":"10.1016\/B0-08-044854-2\/00964-0"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","article-title":"Classifier Chains for Multi-Label Classification","volume":"85","author":"Read","year":"2011","journal-title":"Mach. Learn."},{"key":"ref_37","unstructured":"Sun, A., and Lim, E.-P. (December, January 29). Hierarchical Text Classification and Evaluation. Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Narushynska, O., Teslyuk, V., Doroshenko, A., and Arzubov, M. (2024). Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8040041"},{"key":"ref_39","unstructured":"Kosmopoulos, A., Paliouras, G., and Androutsopoulos, I. (2015). Probabilistic Cascading for Large Scale Hierarchical Classification. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kononenko, I., and Kukar, M. (2007). Measures for Evaluating the Quality of Attributes. Machine Learning and Data Mining, Elsevier.","DOI":"10.1533\/9780857099440"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1007\/s10618-014-0382-x","article-title":"Evaluation Measures for Hierarchical Classification: A Unified View and Novel Approaches","volume":"29","author":"Kosmopoulos","year":"2015","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Plaud, R., Labeau, M., Saillenfest, A., and Bonald, T. (2024). Revisiting Hierarchical Text Classification: Inference and Metrics 2024, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2024.conll-1.18"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Warwick, R.M. (2008). Average Taxonomic Diversity and Distinctness. Encyclopedia of Ecology, Elsevier.","DOI":"10.1016\/B978-008045405-4.00087-2"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"62762","DOI":"10.1109\/ACCESS.2020.2985255","article-title":"A New Splitting Criterion for Better Interpretable Trees","volume":"8","author":"Hwang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Naik, A., and Rangwala, H. (2017). Inconsistent Node Flattening for Improving Top-down Hierarchical Classification. arXiv.","DOI":"10.1109\/DSAA.2016.47"},{"key":"ref_46","first-page":"497","article-title":"Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study","volume":"Volume 12266","author":"Yang","year":"2020","journal-title":"Medical Image Computing and Computer Assisted Intervention"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Doroshenko, A., and Tkachenko, R. (2018, January 11\u201314). Classification of Imbalanced Classes Using the Committee of Neural Networks. Proceedings of the 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/STC-CSIT.2018.8526611"},{"key":"ref_48","unstructured":"(2024, December 17). Product Categories. Available online: https:\/\/www.directionsforme.org\/categories."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vilar, D., and Federico, M. (2021, January 5\u20136). A Statistical Extension of Byte-Pair Encoding. Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021), Bangkok, Thailand.","DOI":"10.18653\/v1\/2021.iwslt-1.31"},{"key":"ref_50","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, Association for Computational Linguistics."},{"key":"ref_51","first-page":"57","article-title":"Preprocessing Product Descriptions with Byte Pair Encoding: A Solution for Abbreviation-Heavy Texts\/\/CEUR Workshop Proceedings","volume":"Volume 3861","author":"Teslyuk","year":"2024","journal-title":"Proceedings of the Computational Intelligence Application Workshop (CIAW 2024)"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"108197","DOI":"10.1016\/j.patcog.2021.108197","article-title":"A Unified Hierarchical XGBoost Model for Classifying Priorities for COVID-19 Vaccination Campaign","volume":"121","author":"Romeo","year":"2022","journal-title":"Pattern Recognit."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/65\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:50:24Z","timestamp":1760028624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["bdcc9030065"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9030065","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,11]]}}}