A classification for complex imbalanced data in disease screening and early diagnosis
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9442
Abstract: Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high‐dimensional longitudinal… read more here.
Keywords: classification; disease; diagnosis; imbalanced classification ... See more keywords
Genetic programming for high-dimensional imbalanced classification with a new fitness function and program reuse mechanism
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-020-05056-7
Abstract: Genetic programming (GP) has been successfully applied to classification. However, GP may evolve biased classifiers when encountering the problem of class imbalance. These biased classifiers are often not reliable to be applied to some real-world… read more here.
Keywords: high dimensional; classification; class; imbalanced classification ... See more keywords
Few-shot imbalanced classification based on data augmentation
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DOI: 10.1007/s00530-021-00827-0
Abstract: Few-shot imbalanced classification tasks are commonly faced in the real-world applications due to the unbalanced data distribution and few samples of rare classes. As known, the traditional machine learning algorithms perform poorly on the imbalanced… read more here.
Keywords: shot; shot imbalanced; imbalanced classification; data augmentation ... See more keywords
Imbalanced classification in sparse and large behaviour datasets
Sign Up to like & getrecommendations! Published in 2017 at "Data Mining and Knowledge Discovery"
DOI: 10.1007/s10618-017-0517-y
Abstract: Recent years have witnessed a growing number of publications dealing with the imbalanced learning issue. While a plethora of techniques have been investigated on traditional low-dimensional data, little is known on the effect thereof on… read more here.
Keywords: large behaviour; sparse large; behaviour datasets; classification sparse ... See more keywords
IRIC: An R library for binary imbalanced classification
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DOI: 10.1016/j.softx.2019.100341
Abstract: Abstract Imbalanced classification is a challenging issue in data mining and machine learning, for which a large number of solutions have been proposed. In this paper, we introduce an R library called IRIC , which… read more here.
Keywords: binary imbalanced; classification; classification iric; iric library ... See more keywords
Adaptive FH-SVM for Imbalanced Classification
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DOI: 10.1109/access.2019.2940983
Abstract: Support vector machines (SVMs), powerful learning methods, have been popular among machine learning researches due to their strong performance on both classification and regression problems. However, traditional SVM making use of Hinge Loss cannot deal… read more here.
Keywords: svm; loss; focal loss; class ... See more keywords
Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2022.3225404
Abstract: Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students’ performance based on their prior academic grades. Concerning the significance of this area,… read more here.
Keywords: grade prediction; student; student grade; imbalanced classification ... See more keywords
A Distance-Based Weighted Undersampling Scheme for Support Vector Machines and its Application to Imbalanced Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2017.2755595
Abstract: A support vector machine (SVM) plays a prominent role in classic machine learning, especially classification and regression. Through its structural risk minimization, it has enjoyed a good reputation in effectively reducing overfitting, avoiding dimensional disaster,… read more here.
Keywords: classification; support vector; distance; undersampling scheme ... See more keywords
Learning misclassification costs for imbalanced classification on gene expression data
Sign Up to like & getrecommendations! Published in 2019 at "BMC Bioinformatics"
DOI: 10.1186/s12859-019-3255-x
Abstract: Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and… read more here.
Keywords: classification; cost weights; cost; optimal cost ... See more keywords
An empirical evaluation of sampling methods for the classification of imbalanced data
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DOI: 10.1371/journal.pone.0271260
Abstract: In numerous classification problems, class distribution is not balanced. For example, positive examples are rare in the fields of disease diagnosis and credit card fraud detection. General machine learning methods are known to be suboptimal… read more here.
Keywords: performance; classification; machine learning; imbalanced classification ... See more keywords