4.7 Article

A semi-supervised resampling method for class-imbalanced learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Semi-Supervised Clustering Under a Compact-Cluster Assumption

Zhen Jiang et al.

Summary: Semi-supervised clustering aims to utilize prior knowledge to improve clustering performance. Existing methods do not adequately consider the natural gap between class information and clustering when using partial labeling information. In order to address this issue, a compact-cluster assumption is proposed along with a general framework called CSSC, which supervises traditional clustering using an objective function that measures the compactness of clusters. Two effective solutions for Kmeans and spectral clustering are provided within this framework. The proposed method is shown to be feasible and effective through theoretical analyses and extensive experiments on real-world datasets.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Article Computer Science, Artificial Intelligence

Improved deep convolutional embedded clustering with re-selectable sample training

Hu Lu et al.

Summary: This paper proposes an improved deep convolutional embedded clustering algorithm (IDCEC) that improves clustering performance by using reliable samples. Experimental results show that the proposed method outperforms traditional clustering algorithms and state-of-the-art deep clustering algorithms under multiple clustering evaluation metrics.

PATTERN RECOGNITION (2022)

Article Computer Science, Information Systems

Annealing Genetic GAN for Imbalanced Web Data Learning

Jingyu Hao et al.

Summary: Class imbalance is a fundamental and important problem in web data, and the key to overcoming it is data augmentation by increasing the effective instances of the minority class. This paper proposes a new training strategy called AGGAN, which uses simulated annealing genetic algorithm to avoid GANs getting stuck in local optima when dealing with imbalanced data. Experimental results demonstrate that AGGAN effectively solves the class imbalance problem and reduces training issues in existing GANs.

IEEE TRANSACTIONS ON MULTIMEDIA (2022)

Article Computer Science, Artificial Intelligence

Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

Justin Engelmann et al.

Summary: This paper explores the potential of using Generative Adversarial Networks (GANs) for oversampling, focusing on a conditional Wasserstein GAN approach for modeling tabular datasets with numerical and categorical variables. The study shows that GAN-based oversampling is competitive in the context of credit scoring compared to standard oversampling methods, suggesting that GAN architectures for tabular data are valuable tools for data scientists.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

Relevant information undersampling to support imbalanced data classification

J. Hoyos-Osorio et al.

Summary: This paper introduces a Relevant Information-based UnderSampling (RIUS) approach to enhance classification performance for imbalanced data scenarios. Experimental results demonstrate that RIUS and its enhancement CRIUS effectively reduce information loss.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

CDBH: A clustering and density-based hybrid approach for imbalanced data classification

Behzad Mirzaei et al.

Summary: The problem of imbalanced data set classification is common in machine learning and data mining research. Preprocessing the data distribution is an effective method to address this issue, and in this paper, a hybrid approach called CDBH is proposed. The experiment results over 44 datasets show the superiority of CDBH method based on the F-measure criterion.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Computer Science, Artificial Intelligence

LoRAS: an oversampling approach for imbalanced datasets

Saptarshi Bej et al.

Summary: This article introduces a method, LoRAS, that overcomes the limitations of SMOTE oversampling technique, and through experiments, proves that LoRAS generates better machine learning models on imbalanced datasets, improving F1-Score and balanced accuracy. Compared to most SMOTE extensions, LoRAS achieves better results in generating classification models.

MACHINE LEARNING (2021)

Article Computer Science, Artificial Intelligence

A survey on semi-supervised learning

Jesper E. Van Engelen et al.

MACHINE LEARNING (2020)

Article Computer Science, Information Systems

Neighbourhood-based undersampling approach for handling imbalanced and overlapped data

Pattaramon Vuttipittayamongkol et al.

INFORMATION SCIENCES (2020)

Article Computer Science, Information Systems

Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering

Xinmin Tao et al.

INFORMATION SCIENCES (2020)

Article Computer Science, Artificial Intelligence

A reduced universum twin support vector machine for class imbalance learning

B. Richhariya et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Deep learning for symbols detection and classification in engineering drawings

Eyad Elyan et al.

NEURAL NETWORKS (2020)

Article Computer Science, Artificial Intelligence

Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems

Zhe Wang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Information Systems

Under-sampling class imbalanced datasets by combining clustering analysis and instance selection

Chih-Fong Tsai et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Artificial Intelligence

Cost-sensitive support vector machines

Arya Iranmehr et al.

NEUROCOMPUTING (2019)

Article Computer Science, Artificial Intelligence

Auto-weighted multi-view constrained spectral clustering

Chuan Chen et al.

NEUROCOMPUTING (2019)

Article Computer Science, Information Systems

Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

Georgios Douzas et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Artificial Intelligence

TLUSBoost algorithm: a boosting solution for class imbalance problem

Sujit Kumar et al.

SOFT COMPUTING (2019)

Article Computer Science, Artificial Intelligence

Partition Level Constrained Clustering

Hongfu Liu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Article Computer Science, Information Systems

A hybrid evolutionary preprocessing method for imbalanced datasets

Ginny Y. Wong et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Information Systems

Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE

Georgios Douzas et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Artificial Intelligence

Adaptive Learning-Based k-Nearest Neighbor Classifiers With Resilience to Class Imbalance

Sankha Subhra Mullick et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Computer Science, Information Systems

Clustering-based undersampling in class-imbalanced data

Wei-Chao Lin et al.

INFORMATION SCIENCES (2017)

Article Computer Science, Interdisciplinary Applications

Semi-supervised k-means plus

Jordan Yoder et al.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION (2017)

Article Computer Science, Artificial Intelligence

Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem

Nir Ofek et al.

NEUROCOMPUTING (2017)

Article Automation & Control Systems

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification

Qi Kang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2017)

Article Automation & Control Systems

Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning

Pin Lim et al.

IEEE TRANSACTIONS ON CYBERNETICS (2017)

Article Computer Science, Artificial Intelligence

Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets

Iman Nekooeimehr et al.

EXPERT SYSTEMS WITH APPLICATIONS (2016)

Article Computer Science, Artificial Intelligence

MWMOTE-Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning

Sukarna Barua et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)

Article Computer Science, Artificial Intelligence

Semi-Supervised Kernel Mean Shift Clustering

Saket Anand et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014)

Article Computer Science, Artificial Intelligence

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

Huaxiang Zhang et al.

INFORMATION FUSION (2014)

Article Computer Science, Artificial Intelligence

A study on semi-supervised FCM algorithm

Shan Zeng et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2013)

Article Computer Science, Artificial Intelligence

EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Mikel Galar et al.

PATTERN RECOGNITION (2013)

Article Computer Science, Artificial Intelligence

DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique

Chumphol Bunkhumpornpat et al.

APPLIED INTELLIGENCE (2012)

Article Computer Science, Artificial Intelligence

SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

Enislay Ramentol et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

Salvador Garcia et al.

EVOLUTIONARY COMPUTATION (2009)

Article Computer Science, Artificial Intelligence

Cluster-based under-sampling approaches for imbalanced data distributions

Show-Jane Yen et al.

EXPERT SYSTEMS WITH APPLICATIONS (2009)

Article Computer Science, Artificial Intelligence

Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

DC Tao et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)