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Interpretable ML for Imbalanced Data
, 2022 Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear.
Dablain, Damien A. +4 more
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Research and application of XGBoost in imbalanced data
International Journal of Distributed Sensor Networks, 2022 As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but its classification effect in the case of data imbalance is often not ideal.
Ping Zhang, Yiqiao Jia, Youlin Shang
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Journal of Applied Sciences and Environmental Management, 2022 Clinicians are required to make an early prediction of diseases to save a life, especially cerebrovascular diseases. The objective of this research is to use mathematical models such as boosting machine learning algorithms as a tool to be applied by ...
S. D. Abdullahi, S. A. Muhammad
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Class Rectification Hard Mining for Imbalanced Deep Learning
, 2017 Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes.
Dong, Qi, Gong, Shaogang, Zhu, Xiatian
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Mine Classification With Imbalanced Data [PDF]
IEEE Geoscience and Remote Sensing Letters, 2009 In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly.
D.P. Williams, V. Myers, M.S. Silvious
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Imbalanced data classification using MapReduce and relief
Journal of Information and Telecommunication, 2018 Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance.
Joanna Jedrzejowicz +3 more
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, 2017 Lung nodule classification is a class imbalanced problem, as nodules are found with much lower frequency than non-nodules. In the class imbalanced problem, conventional classifiers tend to be overwhelmed by the majority class and ignore the minority ...
Nakano, Hiroki +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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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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On Improving the Classification of Imbalanced Data
Cybernetics and Information Technologies, 2017 Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered ...
Mathews Lincy Meera, Seetha Hari
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