Search Results - Handling Imbalanced Data in Classification Problems

An Efficient Method for Mining Closed Potential High-Utility Itemsets by Bay Vo, Loan T. T. Nguyen, Nguyen Bui, Trinh Duy Nguyen, Van‐Nam Huynh, Tzung-Pei Hong

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Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem by Desuky, Abeer S., Elbarawy, Yomna M., Kausar, Samina, Omar, Asmaa Hekal, Hussain, Sadiq

“…The imbalanced datasets and their classification has pulled in as a hot research topic over the years…”
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HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source by Wan, Shixiang, Duan, Yucong, Zou, Quan

“… Second, techniques for handling imbalanced data in multi‐label classification…”
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Small sphere and large margin support tensor machines for imbalanced tensor data classification by Liu, Hexuan, Li, Xiao, Xu, Yitian

“…The small sphere and large margin approach (SSLM) is a representative learning algorithm for handling imbalanced data classification problems…”
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Handling Imbalanced Data Classification Problem using Artificial Immune System with Mahalanobis Distance by Jitkongchuen, Duangjai, Sukpongthai, Warattha

“…Most of the data in the classification domain are imbalanced data, meaning that the answers of each class are not the…”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects by Shing Chiang Tan, Watada, Junzo, Ibrahim, Zuwairie, Khalid, Marzuki

“… Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution…”
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A histogram SMOTE-based sampling algorithm with incremental learning for imbalanced data classification by Liaw, Lawrence Chuin Ming, Tan, Shing Chiang, Goh, Pey Yun, Lim, Chee Peng

“…) network for handling imbalanced data classification problems.•The limitations of SMOTE are tackled by an organised approach (i.e., histogram…”
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Class imbalanced problem: Taxonomy, open challenges, applications and state-of-the-art solutions by Bhat, Khursheed Ahmad, Sofi, Shabir Ahmad

“… The data imbalance presents major hurdles for classification and prediction problems in machine learning, restricting data analytics and acquiring relevant insights in practically all real-world research domains…”
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Whirlwind Classification with Imbalanced Upper Air Data Handling using SMOTE Algorithm and SVM Classifier by Novitasari, D C R, Foeady, A Z, Nariswari, R, Asyhar, A H, Ulinnuha, N, Farida, Y, Santi, D R, Ilham, Setiawan, F

“… The purpose of this research is to optimize SVM classification with SMOTE algorithm to handling problems in imbalanced data and this research can minimize casualties and losses or also…”
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A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation by Khan, Azal Ahmad, Chaudhari, Omkar, Chandra, Rohitash

“…Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other…”
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Integrating cluster analysis with granular computing for imbalanced data classification problem – A case study on prostate cancer prognosis by Kuo, R.J., Su, P.Y., Zulvia, Ferani E., Lin, C.C.

“…) concept for handling imbalanced dataset. The proposed algorithm assembles data from majority classes into granules to balance the class ratio within the data…”
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How Far Have We Progressed in the Sampling Methods for Imbalanced Data Classification? An Empirical Study by Sun, Zhongbin, Zhang, Jingqi, Zhu, Xiaoyan, Xu, Donghong

“… A variety of methods have been proposed for imbalanced data classification, and data sampling methods are more prevalent due to their independence from classification algorithms…”
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Imbalanced traffic identification using an imbalanced data gravitation-based classification model by Peng, Lizhi, Zhang, Haibo, Chen, Yuehui, Yang, Bo

“… Data gravitation-based classification (DGC) is a new classification model for handling imbalance data sets and we proposed an imbalanced DGC (IDGC…”
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Survey on Highly Imbalanced Multi-class Data by Hamid, Mohd Hakim Abdul, Yusoff, Marina, Mohamed, Azlinah

“… From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems…”
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Handling imbalanced medical image data: A deep-learning-based one-class classification approach by Gao, Long, Zhang, Lei, Liu, Chang, Wu, Shandong

“…[Display omitted] •A novel deep-learning-based model for the data imbalance problem…”
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Handling the Problem of Unbalanced Data Sets in the Classification of Technical Equipment States by Obukhov Egor

“…Questions of handling unbalanced data considered in this article. As models for classification, PNN and MLP are used…”
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On the class overlap problem in imbalanced data classification by Vuttipittayamongkol, Pattaramon, Elyan, Eyad, Petrovski, Andrei

“… This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy…”
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Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization by Rosales-Perez, Alejandro, Garcia, Salvador, Herrera, Francisco

“… This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support…”
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Analyzing drop coalescence in microfluidic devices with a deep learning generative model by Zhu, Kewei, Cheng, Sibo, Kovalchuk, Nina, Simmons, Mark, Guo, Yi-Ke, Matar, Omar K, Arcucci, Rossella

“… However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem…”
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Handling imbalanced medical datasets: review of a decade of research by Salmi, Mabrouka, Atif, Dalia, Oliva, Diego, Abraham, Ajith, Ventura, Sebastian

“… This article comprehensively reviews advances in addressing imbalanced medical datasets over the past decade, offering a novel classification of approaches into preprocessing, learning levels, and combined techniques…”
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