{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:12:39Z","timestamp":1780441959939,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,14]],"date-time":"2022-03-14T00:00:00Z","timestamp":1647216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0704400"],"award-info":[{"award-number":["2018YFB0704400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program of Science and Technology of Yunnan Province","award":["202002AB080001-2"],"award-info":[{"award-number":["202002AB080001-2"]}]},{"name":"Key Research Project of Zhejiang Laboratory","award":["2021PE0AC02"],"award-info":[{"award-number":["2021PE0AC02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Many machine learning problem domains, such as the detection of fraud, spam, outliers, and anomalies, tend to involve inherently imbalanced class distributions of samples. However, most classification algorithms assume equivalent sample sizes for each class. Therefore, imbalanced classification datasets pose a significant challenge in prediction modeling. Herein, we propose a density-based random forest algorithm (DBRF) to improve the prediction performance, especially for minority classes. DBRF is designed to recognize boundary samples as the most difficult to classify and then use a density-based method to augment them. Subsequently, two different random forest classifiers were constructed to model the augmented boundary samples and the original dataset dependently, and the final output was determined using a bagging technique. A real-world material classification dataset and 33 open public imbalanced datasets were used to evaluate the performance of DBRF. On the 34 datasets, DBRF could achieve improvements of 2\u201315% over random forest in terms of the F1-measure and G-mean. The experimental results proved the ability of DBRF to solve the problem of classifying objects located on the class boundary, including objects of minority classes, by taking into account the density of objects in space.<\/jats:p>","DOI":"10.3390\/fi14030090","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T03:06:10Z","timestamp":1647313570000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Density-Based Random Forest for Imbalanced Data Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Jia","family":"Dong","sequence":"first","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, 99 Shangda Rd., Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Engineering & Science, Shanghai University, 99 Shangda Rd., Shanghai 200444, China"},{"name":"Materials Genome Institute, Shanghai University, 99 Shangda Rd., Shanghai 200444, China"},{"name":"Zhejiang Laboratory, Hangzhou 311100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/2047-2501-1-15","article-title":"On the application of multi-class classification in physical therapy recommendation","volume":"1","author":"Zhang","year":"2013","journal-title":"Health Sci. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, H., Zhang, X., and Qi, D. (2018, January 8\u201311). Deep learning intrusion detection model based on optimized imbalanced network data. Proceedings of the 2018 IEEE 18th International Conference on Communication Technology (ICCT), Chongqing, China.","DOI":"10.1109\/ICCT.2018.8600219"},{"key":"ref_3","unstructured":"Bian, Y., Cheng, M., Yang, C., Yuan, Y., Li, Q., Zhao, J.L., and Liang, L. (July, January 27). Financial fraud detection: A new ensemble learning approach for imbalanced data. Proceedings of the 20th Pacific Asia Conference on Information Systems (PACIS 2016), Chiayi, Taiwan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1093\/bioinformatics\/btl027","article-title":"Enhancing instance-based classification with local density: A new algorithm for classifying unbalanced biomedical data","volume":"22","author":"Plant","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yap, B.W., Rani, K.A., Rahman, H.A.A., Fong, S., Khairudin, Z., and Abdullah, N.N. (2013, January 16\u201318). An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), Kuala Lumpur, Malaysia.","DOI":"10.1007\/978-981-4585-18-7_2"},{"key":"ref_6","first-page":"321","article-title":"Smote: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1007\/s10489-011-0287-y","article-title":"Dbsmote: Density-based synthetic minority over-sampling technique","volume":"36","author":"Bunkhumpornpat","year":"2012","journal-title":"Appl. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ma, L., and Fan, S. (2017). Cure-smote algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1578-z"},{"key":"ref_9","first-page":"185","article-title":"Fault detection method of electronic equipment based on sl-smote and cs-rvm","volume":"55","author":"Gao","year":"2019","journal-title":"Comput. Eng. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W.-Y., and Mao, B.-H. (2005, January 23\u201326). Borderline-smote: A new over-sampling method in imbalanced datasets learning. Proceedings of the International Conference on Intelligent Computing, Hefei, China.","DOI":"10.1007\/11538059_91"},{"key":"ref_11","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TSMC.1976.4309452","article-title":"Two modifications of cnn","volume":"SMC-6","author":"Tomek","year":"1976","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_14","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: One-sided selection","volume":"97","author":"Kubat","year":"1997","journal-title":"Icml"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume":"SMC-2","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Laurikkala, J. (2001, January 1\u20134). Improving identification of difficult small classes by balancing class distribution. Proceedings of the Conference on Artificial Intelligence in Medicine in Europe, Cascais, Portugal.","DOI":"10.1007\/3-540-48229-6_9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TKDE.2006.17","article-title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","volume":"18","author":"Zhou","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_18","unstructured":"Zhou, Z.-H. (2020). Ensemble Learning: Foundations and Algorithms, Electronic Industry Press."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1007730.1007739","article-title":"Extreme re-balancing for svms: A case study","volume":"6","author":"Raskutti","year":"2004","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Lazarevic, A., Hall, L.O., and Bowyer, K.W. (2003, January 15\u201319). Smoteboost: Improving prediction of the minority class in boosting. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Antwerp, Belgium.","DOI":"10.1007\/978-3-540-39804-2_12"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Ournal Comput. And Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.ins.2020.12.023","article-title":"A hybrid data-level ensemble to enable learning from highly imbalanced dataset","volume":"554","author":"Chen","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_23","first-page":"97","article-title":"Adacost: Misclassification cost-sensitive boosting","volume":"99","author":"Fan","year":"1999","journal-title":"Icml"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1108\/03684921311295547","article-title":"Boosting: Foundations and algorithms","volume":"42","author":"Schapire","year":"2013","journal-title":"Kybernetes"},{"key":"ref_25","unstructured":"Chen, C., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, University of California."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114041","DOI":"10.1016\/j.eswa.2020.114041","article-title":"A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning","volume":"164","author":"Choudhary","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","first-page":"2163","article-title":"Biased random forest for dealing with the class imbalance problem","volume":"30","author":"Teitei","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105738","DOI":"10.1016\/j.knosys.2020.105738","article-title":"A novel random forest approach for imbalance problem in crime linkage","volume":"195","author":"Li","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"E352","DOI":"10.7717\/peerj-cs.352","article-title":"Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing","volume":"7","author":"Oyewola","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hemalatha, J., Roseline, S.A., Geetha, S., Kadry, S., and Damaeviius, R. (2021). An Efficient DenseNet-Based Deep Learning Model for Malware Detection. Entropy, 23.","DOI":"10.3390\/e23030344"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.3906\/elk-2101-133","article-title":"Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold","volume":"2021","author":"Alli","year":"2021","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Nasir, I.M., Khan, M.A., Yasmin, M., Shah, J.H., and Damasevicius, R. (2020). Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training. Sensors, 20.","DOI":"10.3390\/s20236793"},{"key":"ref_33","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"Ester","year":"1996","journal-title":"Kdd"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00170-018-2726-y","article-title":"Micro machining of bulk metallic glasses: A review","volume":"100","author":"Zhang","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_35","unstructured":"Dua, D., and Graff, C. (2012, May 08). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_36","first-page":"255","article-title":"Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework","volume":"17","author":"Fernandez","year":"2011","journal-title":"Crit. Rev. Solid State Mater. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/jfb9010019","article-title":"A critical review on metallic glasses as structural materials for cardiovascular stent applications","volume":"9","author":"Mehdi","year":"2018","journal-title":"J. Funct. Biomater."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/10408436.2017.1358149","article-title":"Recent advancements in bulk metallic glasses and their applications: A review","volume":"43","author":"Khan","year":"2018","journal-title":"Crit. Rev. Solid State Mater. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.3934\/matersci.2016.3.1022","article-title":"Process, structure, property and applications of metallic glasses","volume":"3","author":"Nair","year":"2016","journal-title":"AIMS Mater. Sci."},{"key":"ref_40","unstructured":"Zhou, Z.-H. (2016). Machine Learning, Tsinghua University Press."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/3\/90\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:36:00Z","timestamp":1760135760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/3\/90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,14]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["fi14030090"],"URL":"https:\/\/doi.org\/10.3390\/fi14030090","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,14]]}}}