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DPGCN Model: A Novel Fault Diagnosis Method for Marine Diesel Engines Based on Imbalanced Datasets
IEEE Transactions on Instrumentation and Measurement, 2023The class imbalance problem is prevalent in the condition monitoring (CM) data of marine diesel engines. That significantly deteriorates the diagnostic performance of a data-driven fault diagnosis.
Ruihan Wang, Hui Chen, C. Guan
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Implications of imbalanced datasets for empirical ROC-AUC estimation in binary classification tasks
Journal of Statistical Computation and Simulation, 2023The area under the curve (AUC) is the most popular measure for summarizing a binary classifier's receiver operating characteristic (ROC) curve. Therefore, it is essential to ensure that the AUC estimation is accurate.
Yujian Liu, Yazhe Li, Dejun Xie
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Epileptic Seizure Prediction for Imbalanced Datasets
2019 Medical Technologies Congress (TIPTEKNO), 2019In this study, the methods used in the classification of imbalanced data sets were applied to EEG signals obtained from epilepsy patients and epileptic seizures were estimated. Firstly, the data set was balanced by using under-sampling, oversampling, and synthetic minority over-sampling technique and classified with Support Vector Machines.
Coşgun, Ercan +2 more
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Detection of malicious javascript on an imbalanced dataset
Internet of Things, 2021Abstract In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results. These methods focus on achieving a light-weight filtering model that can quickly and precisely filter out malicious data for dynamic analysis.
Phung Minh Ngoc, Mamoru Mimura
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Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2022
Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more
R. Priya +5 more
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Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more
R. Priya +5 more
semanticscholar +1 more source
Rare events and imbalanced datasets: an overview
International Journal of Data Mining, Modelling and Management, 2011Accurate prediction is important in data mining and data classification. Rare events data, imbalanced or skewed datasets are very important in data mining and classification. However, These types of data are difficult to predict and to explain as has been demonstrated in the literature. The problems arise from various sources.
Maher Maalouf, Theodore B. Trafalis
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Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets
2013Many efforts have been done recently proposing new intelligent resampling methods as a way to solve class imbalance problems; one of the main challenges of the machine learning community nowadays. Usually the purpose of these methods is to balance the classes. However, there are works in the literature showing that those methods can also be suitable to
Olatz Arbelaitz +3 more
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Supervised Microalgae Classification in Imbalanced Dataset
2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 2016Microalgae are unicellular organisms that have physical characteristics such as size, shape or even the present structures. Classifying them manually may require great effort from experts since thousands of microalgae can be found in a small sample of water. Furthermore, the manual classification is not a trivial operation.
Iago Lourenço Correa +3 more
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Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets
Comput. Biol. Medicine, 2022Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that has ...
Manisha Saini, Seba Susan
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A Robust Classifier for Imbalanced Datasets
2014Imbalanced dataset classification is a challenging problem, since many classifiers are sensitive to class distribution so that the classifiers’ prediction has bias towards majority class. Hellinger Distance has been proven that it is skew-insensitive and the decision trees that employ Hellinger Distance as a splitting criterion have shown better ...
Sori Kang, Kotagiri Ramamohanarao
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