Results 11 to 20 of about 224,004 (328)
A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models
Axioms, 2022 Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine ...
Ming Zheng +5 more
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Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
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Multicriteria Classifier Ensemble Learning for Imbalanced Data
IEEE Access, 2022 One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that ...
Weronika Wegier +2 more
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Bicriteria Oversampling for Imbalanced Data Classification
Procedia Computer Science, 2022 The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
openaire +1 more source
A method for increasing accuracy of credit imbalanced data [PDF]
فصلنامه بورس اوراق بهادارThe main goal of this research is to provide a method that can be used to increase the accuracy of credit imbalance data. Financial fraud is a fundamental problem that affects both the financial sector and life and plays an important role in affecting ...
Arash GhorbanniaDelavar +1 more
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Hellinger Distance Trees for Imbalanced Streams [PDF]
, 2014 Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem.
Brooke, J. M. +3 more
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Framework for imbalanced data classification
Procedia Computer Science, 2021 Classifying imbalanced data remains a challenging task. The paper presents a framework for imbalanced datasets classification which makes use of different methods of oversampling and methods of dynamical selection of classifiers. The framework allows to perform extensive experiments to determine best possible configuration for the examined dataset in ...
Mikołaj Błaszczyk +1 more
+4 more sources
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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A Novel Imbalanced Ensemble Learning in Software Defect Predication
IEEE Access, 2021 With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng +4 more
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A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem
Applied Computer Systems, 2019 The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling ...
Terzi Duygu Sinanc, Sagiroglu Seref
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