Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets

Sadhukhan, Payel; Palit, Sarbani

doi:10.1016/j.patrec.2019.08.009

Cited by 34 publications

(9 citation statements)

References 29 publications

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“…Sáez et al (2016) applied this method in analyzing class characteristics, whereby the subsets of certain instances were identified in each class and increased individually. A novel reversenearest neighborhood-based over-sampling method for the class imbalance of a multilabel dataset was introduced by Sadhukhan and Palit (2019). All those points that included the query point as one of their neighbors had the reverse nearest neighborhood of a query point.…”

Section: Related Workmentioning

confidence: 99%

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

Taha

Tiun

Rahman

et al. 2021

Journal of Information and Communication Technology

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Simultaneous multiple labelling of documents, also known as multilabel text classification, will not perform optimally if the class is highly imbalanced. Class imbalanced entails skewness in the fundamental data for distribution that leads to more difficulty in classification. Random over-sampling and under-sampling are common approaches to solve the class imbalanced problem. However, these approaches have several drawbacks; the under-sampling is likely to dispose of useful data, whereas the over-sampling can heighten the probability of overfitting. Therefore, a new method that can avoid discarding useful data and overfitting problems is needed. This study proposes a method to tackle the class imbalanced problem by combining multilabel over-sampling and under-sampling with class alignment (ML-OUSCA). In the proposed ML-OUSCA, instead of using all the training instances, it draws a new training set by over-sampling small size classes and under-sampling big size classes. To evaluate our proposed ML-OUSCA, evaluation metrics of average precision, average recall and average F-measure on three benchmark datasets, namely, Reuters-21578, Bibtex, and Enron datasets, were performed. Experimental results showed that the proposed ML-OUSCA outperformed the chosen baseline random resampling approaches; K-means SMOTE and KNN-US. Thus, based on the results, we can conclude that designing a resampling method based on the class imbalanced together with class alignment will improve multilabel classification even better than just the random resampling method.

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Section: Related Workmentioning

confidence: 99%

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

Taha

Tiun

Rahman

et al. 2021

Journal of Information and Communication Technology

View full text Add to dashboard Cite

show abstract

“…One way of achieving this is through the removal of points from the majority class of each label-for example using random undersampling [30] or tomek-link based undersampling [22]. Another way to achieve this is by adding synthetic minority points to the minority class [17,28,4]. Although this approaches have been shown to be effective there is still a lot of room for improvement.…”

Section: Related Workmentioning

confidence: 99%

Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data

Sadhukhan¹,

Pakrashi²,

Palit³

et al. 2021

Preprint

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There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient multi-label classification algorithms. In this paper, we tackle this problem by proposing a minority class oversampling scheme, UCLSO, which integrates Unsupervised Clustering and Label-Specific data Oversampling. Clustering is performed to find out the key distinct and locally connected regions of a multi-label dataset (irrespective of the label information). Next, for each label, we explore the distributions of minority points in the cluster sets. Only the minority points within a cluster are used to generate the synthetic minority points that are used for oversampling. Even though the cluster set is the same across all labels, the distributions of the synthetic minority points will vary across the labels. The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently. Experiments using 12 multi-label datasets and several multi-label algorithms show that the proposed method performed very well compared to the other competing algorithms.

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“…In [36], authors have integrated the data gravitational model with multi-label lazy learner for improving the minority class performances of imbalanced multi-label datasets. In our very recent work [37], we have used a reverse-nearest neighborhood oversampling to curb the problem of differential imbalance in multi-label datasets. Several other techniques like convex relaxation [38], ensembles of random graph [39] and graph classification [40] are also employed to address multilabel classification.…”

Section: Related Workmentioning

confidence: 99%

“…COCOA specifically addresses class-imbalance problem in multi-label datasets. Additionally, we have also included Reverse-nearest neighborhood based oversampling for multi-label dataset (RNNOML) [37] in this empirical study. MLKNN invokes a set of k-nearest neighbor based classifiers for multi-label datasets.…”

Section: Ranking Lossmentioning

confidence: 99%

Lattice and Imbalance Informed Multi-Label Learning

Sadhukhan

Palit

2020

IEEE Access

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In a multi-label dataset, an instance is given a single representation across all possible labels. Despite the mutual sharing of instances among the labels, the membership of the instances vary from label to label. This diversifies the intrinsic class geometries of the labels. Multi-label datasets are often found to be class-imbalanced as well. The varying membership of the instances coupled with the imbalance phenomenon gives rise to varying imbalance ratios across the labels. We address these two key aspects in this work, Lattice and Imbalance Informed Multi-label Learning (LIIML) in a two step procedure. Firstly, we obtain the imbalance ratios and the intrinsic positive and negative class lattices of each label. We capitalize on these two information to obtain a dedicated feature set for each label. In the second step, to handle the class-imbalance further, we employ a scheme of imbalance-adaptive misclassification cost across the labels. We have evaluated the competence of the proposed method in a generic as well as class-imbalanced framework. The elaborate empirical study establishes the competence of the proposed method in both the contexts.

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Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets

Cited by 34 publications

References 29 publications

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

Multilabel Over-Sampling and Under-Sampling With Class Alignment for Imbalanced Multilabel Text Classification

Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data

Lattice and Imbalance Informed Multi-Label Learning

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