Results 271 to 280 of about 46,171 (307)
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Imbalanced Learning in Massive Phishing Datasets
2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling
Ali Azari +4 more
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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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An investigation of bankruptcy prediction in imbalanced datasets
Decision Support Systems, 2018Abstract Previous studies of bankruptcy prediction in imbalanced datasets analyze either the loss of prediction due to data imbalance issues or treatment methods for dealing with this issue. The current article presents a combined investigation of the degree of imbalance, loss of performance, and treatment methods.
David Veganzones, Eric Séverin
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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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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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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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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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Discrimination aware classification for imbalanced datasets
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute.
Goce Ristanoski +2 more
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An Improved Measurement of the Imbalanced Dataset
2018Imbalanced classification is a classification problem that violates the assumption of uniform distribution of samples. In such problems, traditional imbalanced datasets are measured in terms of the imbalance of sample size, without considering the distribution information, which has a more important impact on the classification performance, so the ...
Chunkai Zhang +5 more
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