Results 21 to 30 of about 39,020 (265)
Asymmetric gradient penalty based on power exponential function for imbalanced data classification
Complex & Intelligent Systems, 2023 Model bias is a tricky problem in imbalanced data classification. An asymmetric gradient penalty method is proposed based on the power exponential function to alleviate this. The methodology integrates a power exponential function as a moderator into the
Linyong Zhou +3 more
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Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
Applied Sciences, 2020 In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring.
Adolfo Rangel-Díaz-de-la-Vega +4 more
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Anomaly Detection Model for Imbalanced Datasets
CoRR, 2020 This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic ...
Régis Houssou, Stephan Robert-Nicoud
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Learning Imbalanced Datasets With Maximum Margin Loss
2021 IEEE International Conference on Image Processing (ICIP), 2021 A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization.
Haeyong Kang, Thang Vu, Chang D. Yoo
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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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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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Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data [PDF]
Journal of Universal Computer Science, 2020 This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data.
Oscar Camacho-Nieto +2 more
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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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Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets
IEEE Access, 2021 In the past few years, Peer-to-Peer lending (P2P lending) has grown rapidly in the world. The main idea of P2P lending is disintermediation and removing the intermediaries like banks.
Yen-Ru Chen +4 more
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