Metric Learning from Imbalanced Data with Generalization Guarantees

Gautheron, Léo; Habrard, Amaury; Morvant, Emilie; Sebban, Marc

doi:10.1016/j.patrec.2020.03.008

Cited by 25 publications

(14 citation statements)

References 25 publications

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“…For the datasets with imbalance ratio ≤ 6, using F1 score we have compared the results with the other standard methods which are frequently used in class imbalance problem in Table 10. The methods include RU [47], ENN [70], NMU [7], CNN [55], prototype generation using K-means clustering (PK) [57], SMOTE [14], Imbalanced Metric Learning (IML) [29], which follows Mahalanobis metric learning algorithm [63], RTHS [1] and AβBSF [31]. Some of these methods are very popular and useful to deal with the class imbalance problem.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Yu et al [67] worked with accelerating evolutionary computation, Cheng et al [17] introduced various model-based evolutionary algorithms (MBEAs) and He et al [34] proposed evolutionary multi-objective optimization and used it on real-world applications. Gautheron et al [29] introduced Mahalanobis metric learning (IML) algorithm. The authors have used the datasets like Pima, Balance, Splice and Heart to evaluate their model's performance.…”

Section: Literature Surveymentioning

confidence: 99%

“…We have included the datasets with low (e.g., like WDBC and heart datasets) to moderate (e.g., Hayes-roth and Page-blocks0 datasets) imbalance ratio as well as with very high imbalance ratio (e.g., Abalone19 and Yeast5 datasets) to establish the robustness of our model i.e., how well RTPSO behaves on the We have redefined the multi-class problems as binary class problems since class imbalance problem is mainly designed for binary classification problem [44]. For this, we have converted a certain combination of classes into minority class and the rest of the classes into the majority class following the similar convention as described in the work [29]. Related information of these two categories of datasets is provided in Tables 1 and 2.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw

Ahmed

Malakar³

et al. 2021

Complex Intell. Syst.

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Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw

Ahmed

Malakar³

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…4) to improve the data space (through relabeling and regrouping the neighbouring data blocks) which separates the data samples with a different class label by a large margin and makes the samples with the same class labels close to each other; 2) calculate the distance between each of the samples from the training set to the testing samples; 3) run this and previous steps through multiple iterations, controlled by a predefined matching ratio. [62] shows that IML yields to a classification performance bound but requires the learned matrix conforms to the Positive Semi-Definite (PSD) constraint; its performance is still unknown for non-linear metrics.…”

Section: Distance Metric Learning Methodsmentioning

confidence: 99%

Experimental Comparison of Classification Methods under Class Imbalance

Chen¹,

Ji²

2021

ICST Transactions on Scalable Information Systems

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The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms -ranging from sampling, distance metric learning, costsensitive learning to ensemble learning approaches -on several datasets from UCI, KEEL and OpenML. The algorithms included DDAE, MWMOTE, SMOTE, RUSBoost, AdaBoost, cost-sensitive decision tree (csDCT), self-paced Ensemble Classifier, MetaCost, CAdaMEC and Iterative Metric Learning (IML). As the substantial bias potentially caused by imbalance classification can be harmful for underrepresented classes which are of critical social and economic values and even lives, the main objective of our study is thus to understand the impact of imbalance ratio and the size of the utilized datasets on the performance of the above-mentioned algorithms. Our experiments show that 1) Sampling methods perform the worst and cannot be used directly for imbalanced classification, since they lack of consideration of neighborhoods based on distance. However, some classifiers can be improved after the balance of class distribution. 2) Cost-sensitive learning models should be utilized when the dataset is less imbalanced, because it is difficult to set an appropriate cost matrix for a specific dataset, which can cause performance fluctuations. 3) IML consistently shows good performance (in terms of F1 and AUCPRC), is resilient to different imbalance ratios but sensitive to the data distribution of the dataset. 4) Ensemble learning techniques generally perform better over other approaches due to their combined intelligence of multiple basic classifiers. 5) In terms of system performance, self-paced Ensemble Classifier performs fairly well with regards to learning time, while IML and DDAE yield the longest learning time; AdaBoost and self-paced Ensemble Classifier are two algorithms require lowest memory usage. We also provide our empirical recommendation for algorithm selection under different requirements and usage scenarios based on our analysis.

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“…Precision, recall, F1-score, and AUC are frequently used as the evaluation metrics when dealing with imbalanced data [32]. Precision and recall measure exactness and completeness, respectively, and demonstrate an inverse relationship.…”

Section: Adam Optimizer and Learning Rate Decaymentioning

confidence: 99%

A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

You

Shen

Chen

et al. 2020

Shock and Vibration

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Intelligent bearing fault diagnosis has received much research attention in the field of rotary machinery systems where miscellaneous deep learning methods are generally applied. Among these methods, convolution neural network is particularly powerful because of its ability to learn fruitful features from the original data. However, normal convolutions cannot fully utilize the information along the data flow while the features are being abstracted in deeper layers. To address this problem, a new supervised learning model is proposed for small sample size bearing fault diagnosis with consideration of imbalanced data. This model, which is developed based on a convolution neural network, has a high generalization ability, and its performance is verified by conducting two experiments that use data collected from a self-made bearing test rig. The proposed model demonstrates a favorable performance and is more effective and robust than other deep learning methods.

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Metric Learning from Imbalanced Data with Generalization Guarantees

Cited by 25 publications

References 25 publications

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Experimental Comparison of Classification Methods under Class Imbalance

A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

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