Results 21 to 30 of about 106,507 (280)
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 ...
Nicolaou, Sergis +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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A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
Abstract and Applied Analysis, 2013 In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
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Plant Identification in a Combined-Imbalanced Leaf Dataset
IEEE Access, 2022 Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of a distinguishable feature of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately
Viraj K. Gajjar +2 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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Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
Al-Mustansiriyah Journal of Science, 2020 Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields.
Liqaa M. Shoohi, Jamila H. Saud
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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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IEEE Access, 2021 A network intrusion detection system (NIDS) is an important technology for cyber security. Recently, machine learning based NIDSs are being actively researched as various machine learning techniques are proposed.
Yeongje Uhm, Wooguil Pak
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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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Frontiers in Animal Science, 2021 Classification of imbalanced datasets of animal behavior has been one of the top challenges in the field of animal science. An imbalanced dataset will lead many classification algorithms to being less effective and result in a higher misclassification ...
Min Jin +2 more
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