Results 31 to 40 of about 2,374,745 (297)
Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets [PDF]
IEEE journal of biomedical and health informatics, 2022 Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images.
Ş. Öztürk, Tolga Cukur
semanticscholar +1 more source
Offline Reinforcement Learning with Imbalanced Datasets
CoRR, 2023 The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper, we specify
Li Jiang 0008 +5 more
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Data Science in Finance and Economics, 2023 In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern.
Lindani Dube, T. Verster
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Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2020 The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method.
Windyaning Ustyannie, Suprapto Suprapto
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Dialog Speech Sentiment Classification for Imbalanced Datasets [PDF]
, 2021 Speech is the most common way humans express their feel- ings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings. Even though this field has seen tremendous advancements in the last two decades, the task of effectively detecting under represented sen ...
Sergis Nicolaou +6 more
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On the Classification of Imbalanced Datasets
International Journal of Computer Applications, 2012 In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the microcalcification classification which is a classical ...
H. S. Sheshadri, Arun KumarM.N
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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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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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Using deep learning for trajectory classification in imbalanced dataset
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets.
Nicksson Ckayo Arrais de Freitas +3 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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