Results 31 to 40 of about 98,421 (281)
Improving Software Defect Prediction in Noisy Imbalanced Datasets
Applied Sciences, 2023 Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise.
Haoxiang Shi +3 more
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Resampling imbalanced data for network intrusion detection datasets
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
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ITM Web of Conferences, 2017 The approach to the classification problem of the imbalanced datasets has been considered. The aim of this research is to determine the effectiveness of the SMOTE algorithm, when it is necessary to improve the classification quality of the SVM classifier,
Demidova Liliya, Klyueva Irina
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Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data
CAAI Transactions on Intelligence Technology, 2021 In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data.
Gaurav Mohindru +2 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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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
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Binary classification for imbalanced datasets using a novel metric method
Egyptian Informatics JournalThis work proposes a kernel amplification method with non-stationary characteristics for binary classification of non-noisy imbalanced datasets. Our methodology features two key innovations, including that a derived non-stationary kernel construction ...
Jian Zheng +3 more
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DefectNET: multi-class fault detection on highly-imbalanced datasets
, 2019 As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias toward larger classes, which tend to be the background in the semantic ...
Anantrasirichai, N., Bull, David
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Box Drawings for Learning with Imbalanced Data [PDF]
, 2014 The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes.
Abe N. +4 more
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Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers [PDF]
International Journal of Computational Intelligence Systems, 2012 Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain.
AnaM. Palacios +2 more
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