Results 31 to 40 of about 141,868 (281)
Applied Sciences, 2021 The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different ...
Ziqi Fan +4 more
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Clustering and Community Detection with Imbalanced Clusters [PDF]
, 2016 Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (
Aksoylar, Cem +2 more
core +1 more source
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
CoRR, 2016 Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and
Lemaitre, Guillaume +2 more
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A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction
IEEE Access, 2019 Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision
Yongming Song, Yi Peng
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Multi-class pattern classification in imbalanced data [PDF]
, 2010 The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training ...
Ghanem, Amal S. +2 more
core +1 more source
Class-Imbalanced Semi-Supervised Learning
CoRR, 2020 Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbalanced class distribution.
Minsung Hyun, Jisoo Jeong, Nojun Kwak
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Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
Applied Sciences, 2019 As software systems become increasingly large, the logic becomes more complex, resulting in a large number of bug reports being submitted to the bug repository daily. Due to tight schedules and limited human resources, developers may not have enough time
Shikai Guo +6 more
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A Super-Bagging Method for Volleyball Action Recognition Using Wearable Sensors
Multimodal Technologies and Interaction, 2020 Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort.
Fasih Haider +6 more
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Learning Classifiers for Imbalanced and Overlapping Data
CoRR, 2022 This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteristics of data that generate this problem.
Shivaditya Shivganesh +3 more
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Semi-supervised Classification Based Mixed Sampling for Imbalanced Data
Open Physics, 2019 In practical application, there are a large amount of imbalanced data containing only a small number of labeled data. In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm ...
Zhao Jianhua, Liu Ning
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