Results 41 to 50 of about 539,097 (330)
Variance Ranking Attributes Selection Techniques for Binary Classification Problem in Imbalance Data
IEEE Access, 2019 Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based
Solomon H. Ebenuwa +3 more
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Journal of Algorithms & Computational Technology, 2023 One common issue with datasets used for supervised classification tasks is data imbalance or the unequal distribution of classes within a dataset. The class imbalance may cause biased machine learning models to favor the dominant class, misclassifying ...
Máximo E Sánchez-Gutiérrez +1 more
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MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
, 2017 Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced.
Ahmed, Sajid +6 more
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Trainable Undersampling for Class-Imbalance Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Undersampling has been widely used in the class-imbalance learning area. The main deficiency of most existing undersampling methods is that their data sampling strategies are heuristic-based and independent of the used classifier and evaluation metric. Thus, they may discard informative instances for the classifier during the data sampling.
Minlong Peng +7 more
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CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
, 2017 Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature.
Ahmed, Sajid +5 more
core +1 more source
Coupling different methods for overcoming the class imbalance problem [PDF]
, 2015 Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is ...
Fantozzi, Carlo +2 more
core +1 more source
Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets [PDF]
, 2009 A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g.
Bermejo, P. +4 more
core +1 more source
Rethinking Class Imbalance in Machine Learning
CoRR, 2023 Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced.
openaire +2 more sources
Germline TP53 Mutations Causing Diamond–Blackfan Anemia: A French Report
Pediatric Blood &Cancer, EarlyView.ABSTRACT Diamond–Blackfan anemia is a rare congenital erythroblastopenia typically caused by mutations in ribosomal protein genes. Recently, gain‐of‐function mutations in TP53 have been identified as a novel cause of Diamond–Blackfan anemia. We report two French patients who both harbored a heterozygous TP53 deletion (NM_000546.5: c.1077delA; p ...
Rafael Moisan +6 more
wiley +1 more source
Techno.Com, 2023 Churn merupakan kondisi dimana seseorang berpindah dari satu layanan ke layanan yang lain. Churn pelanggan menjadi masalah yang meningkat cukup signifikan dan menjadi tantangan utama yang harus dihadapi banyak perusahaan perbankan karena memiki peran ...
Wahyu Nugraha, Muhamad Syarif
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