SMOTified-GAN for class imbalanced pattern classification problems

• URL: http://arxiv.org/abs/2108.03235v1
• Date: Fri, 6 Aug 2021 06:14:05 GMT
• Title: SMOTified-GAN for class imbalanced pattern classification problems
• Authors: Anuraganand Sharma, Prabhat Kumar Singh, Rohitash Chandra
• Abstract summary: We propose a novel two-phase oversampling approach that has the synergy of SMOTE and GAN. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets.
• Score: 0.41998444721319217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance in a dataset is a major problem for classifiers that results in poor prediction with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive training dataset. Generally, the pre-processing technique of oversampling of minority class(es) are used to overcome this deficiency. Our focus is on using the hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalanced problems. We propose a novel two-phase oversampling approach that has the synergy of SMOTE and GAN. The initial data of minority class(es) generated by SMOTE is further enhanced by GAN that produces better quality samples. We named it SMOTified-GAN as GAN works on pre-sampled minority data produced by SMOTE rather than randomly generating the samples itself. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets. Its performance is improved by up to 9\% from the next best algorithm tested on F1-score measurements. Its time complexity is also reasonable which is around $O(N^2d^2T)$ for a sequential algorithm.

Related papers

• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO [0.0]
  This study proposes two novel techniques: GAN-based Oversampling (GBO) and Support Vector Machine-SMOTE-GAN (SSG) The preliminary computational result shows that SSG and GBO performed better on the expanded imbalanced eight benchmark datasets than the original SMOTE.
  arXiv  Detail & Related papers  (2022-10-23T22:17:54Z)
• Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks [0.1074267520911262]
  Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models for achieving satisfactory results. In this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions.
  arXiv  Detail & Related papers  (2022-09-01T07:42:16Z)
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning [57.163525407022966]
  Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. We propose AutoSMOTE, an automated over-sampling algorithm that can jointly optimize different levels of decisions.
  arXiv  Detail & Related papers  (2022-08-26T04:28:01Z)
• IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance [1.854931308524932]
  Non-parametric data augmentation with Generative Adversarial Networks (GANs) offers a promising solution. We propose Imputation Balanced GAN (IB-GAN), a novel method that joins data augmentation and classification in a one-step process via an imputation-balancing approach.
  arXiv  Detail & Related papers  (2021-10-14T15:31:16Z)
• GMOTE: Gaussian based minority oversampling technique for imbalanced classification adapting tail probability of outliers [0.0]
  Data-level approaches mainly use the oversampling methods to solve the problem, such as synthetic minority oversampling Technique (SMOTE) In this paper, we proposed Gaussian based minority oversampling technique (GMOTE) with a statistical perspective for imbalanced datasets. When the GMOTE is combined with classification and regression tree (CART) or support vector machine (SVM), it shows better accuracy and F1-Score.
  arXiv  Detail & Related papers  (2021-05-09T07:04:37Z)
• A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios [23.257891827728827]
  Imbalance in the proportion of training samples belonging to different classes often poses performance degradation of conventional classifiers. We propose a novel three step technique to address imbalanced data.
  arXiv  Detail & Related papers  (2021-03-24T09:58:02Z)
• Doubly Contrastive Deep Clustering [135.7001508427597]
  We present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views. Specifically, for the sample view, we set the class distribution of the original sample and its augmented version as positive sample pairs. For the class view, we build the positive and negative pairs from the sample distribution of the class. In this way, two contrastive losses successfully constrain the clustering results of mini-batch samples in both sample and class level.
  arXiv  Detail & Related papers  (2021-03-09T15:15:32Z)
• On Positive-Unlabeled Classification in GAN [130.43248168149432]
  This paper defines a positive and unlabeled classification problem for standard GANs. It then leads to a novel technique to stabilize the training of the discriminator in GANs.
  arXiv  Detail & Related papers  (2020-02-04T05:59:37Z)
• Self-Adversarial Learning with Comparative Discrimination for Text Generation [111.18614166615968]
  We propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generation. During training, SAL rewards the generator when its currently generated sentence is found to be better than its previously generated samples. Experiments on text generation benchmark datasets show that our proposed approach substantially improves both the quality and the diversity.
  arXiv  Detail & Related papers  (2020-01-31T07:50:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.