Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord)

Nekooeimehr, Iman; Lai-Yuen, Susana K.

doi:10.1016/j.neucom.2016.08.071

Cited by 16 publications

(4 citation statements)

References 33 publications

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“…In addition, new samples are created at the boundaries between adjacent classes to smooth the ordinal nature of the dataset. [7] aims to address the imbalanced ordinal regression by clustering the minority classes and over-sampling them based on their distance firstly, and then ordering the relationship with the samples of other classes. The final size of an oversampling cluster depends on its complexity and initial size in order to generate more synthetic instances for more complex and smaller clusters and fewer instances for more complex and larger clusters.…”

Section: A Ordinal Regressionmentioning

confidence: 99%

“…Besides, new samples are generated near the boundary of two adjacent classes to soften the ordinal structure of the samples [6]. The Cluster-Based Weighted Over-sampling clusters minority classes at first, and then oversamples them based on their distance, and finally sorts the classes [7]. Synthetic Minority oversampling technique to deal exclusively with imbalanced Ordinal Regression (SMOR) is a directionaware oversampling algorithm [8], and it can effectively avoid wrong synthetic samples generation by considering the rank of the classes.…”

Section: Introductionmentioning

confidence: 99%

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Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

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Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems. In this paper, we address an active learning based solution for imbalanced OR problem. We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set. Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible. Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms. To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level. The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems. In addition, we also proved the effectiveness of our algorithms by statistical analysis.

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Section: A Ordinal Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

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“…A new oversampling method called Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord) is proposed in [13] for addressing ordinal regression with imbalanced datasets. It aims to address this problem by first clustering minority classes and then oversampling them based on their distances and ordering relationship to other classes' instances.…”

Section: Related Workmentioning

confidence: 99%

Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

et al. 2017

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This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy.

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“…Authors performed literature review in the context of ordinal classification to find the causes for classifier performance degradation [9], [10]. Authors proposed proposed collinear based oversampling algorithm in the safe and border line region for ordinal classification [11]. Introduced unsupervised oversampling method for ordinal regression [12].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

kasiraja¹,

Vijendran²

2018

IJEECS

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<p>The main objective of this research is to improve the predictive accuracy of classification in ordinal multiclass imbalanced scenario. The methodology attempts to uplift the classifier performance through synthesizing sophisticated objects of immature classes. A novel Adaptive Data Structure based oversampling algorithm is proposed to create synthetic objects and Extreme Learning Machine for Ordinal Regression (ELMOP) classifier is adopted to validate our work. The proposed method generating new objects by analyzing the characteristics and intricacy of immature class objects. On the whole, the data set is divided into training and test data. Training data set is updated with new synthetic objects. The experimental analysis is performed on testing data set to check the efficiency of the proposed methodology by comparing it with the existing work. The performance evaluation is conducted in terms of the parameters called Mean Absolute Error, Maximum Mean Absolute Error, Geometric Mean, Kappa and Average Accuracy. The measures prove that the proposed methodology can produce authentic synthetic objects than the existing techniques. The Proposed technique can synthesize the new effective objects through evaluating the structure of immature class. It boosts the global precision and class wise precision especially preserves rank order of the classes.</p>

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Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord)

Cited by 16 publications

References 33 publications

Active Learning for Imbalanced Ordinal Regression

Active Learning for Imbalanced Ordinal Regression

Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

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