A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Khushi, Matloob; Shaukat, Kamran; Alam, Talha Mahboob; Hameed, Ibrahim A.; Uddin, Shahadat; Luo, Siwei; Yang, Xiaoyan; Reyes, Maranatha Consuelo

doi:10.1109/access.2021.3102399

Cited by 167 publications

(83 citation statements)

References 82 publications

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“…In this study, the following models were utilised for the performance measurement of institutions. Machine learning models are also widely used in the domain of healthcare [25][26][27], robotics [28,29], and business [30,31].…”

Section: Modellingmentioning

confidence: 99%

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A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

et al. 2021

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Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

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Section: Modellingmentioning

confidence: 99%

“…o f correct predictions Total No. o f predictions (25) For binary classification, the accuracy is measured using Equation (25) or Equation (26):…”

Section: Accuracy =mentioning

confidence: 99%

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

et al. 2021

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“…Although the IHT method helps resolve class overlap, it heavily depends on the performance of a single classifier for identifying IHP (Khushi et al, 2021). Employing a poorly-performing base classifier for identifying IHP tends to eliminate a significant number of negative samples, leading to information loss.…”

Section: A Learning From Imbalanced Datamentioning

confidence: 99%

A Novel Multi-Stage Ensemble Model With a Hybrid Genetic Algorithm for Credit Scoring on Imbalanced Data

Jin

Zhang

et al. 2021

IEEE Access

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Credit scoring models are the cornerstone of the modern financial industry. After years of development, artificial intelligence and machine learning have led to the transformation of traditional credit scoring models based on statistics. In this study, a novel multi-stage ensemble model with a hybrid genetic algorithm is proposed to achieve accurate and stable credit prediction. To alleviate the adverse effects of imbalanced data in credit scoring models, the Instance Hardness Threshold method is extended using a majority voting strategy to deal with data imbalance. To eliminate redundant and irrelevant features in the dataset and select well-performing base classifiers, a new hybrid genetic algorithm is proposed to obtain the optimal feature subset and base classifier subset. To aggregate the predictive power of the base classifiers, a stacking approach is adopted to integrate the optimal base classifiers into the ensemble model. The proposed model is tested on three standard imbalanced credit scoring datasets, compared with similar state-of-the-art approaches, and evaluated using four well-known evaluation indicators. The experimental results prove the effectiveness of the proposed model and demonstrate its superiority.

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“…Alam [21] proposed a new model specified for imbalanced datasets of credit card default prediction. Khushi utilize the testing results of 20+ class imbalance models with three types of classifiers to detect the best imbalance techniques for medical datasets [22]. Some works explore the risk factors in machine learning models that influence the class identification in an imbalanced dataset [23][24][25].…”

Section: Related Work 21 Adversarial Neural Networkmentioning

confidence: 99%

Machine Translation in Low-Resource Languages by an Adversarial Neural Network

et al. 2021

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Existing Sequence-to-Sequence (Seq2Seq) Neural Machine Translation (NMT) shows strong capability with High-Resource Languages (HRLs). However, this approach poses serious challenges when processing Low-Resource Languages (LRLs), because the model expression is limited by the training scale of parallel sentence pairs. This study utilizes adversary and transfer learning techniques to mitigate the lack of sentence pairs in LRL corpora. We propose a new Low resource, Adversarial, Cross-lingual (LAC) model for NMT. In terms of the adversary technique, LAC model consists of a generator and discriminator. The generator is a Seq2Seq model that produces the translations from source to target languages, while the discriminator measures the gap between machine and human translations. In addition, we introduce transfer learning on LAC model to help capture the features in rare resources because some languages share the same subject-verb-object grammatical structure. Rather than using the entire pretrained LAC model, we separately utilize the pretrained generator and discriminator. The pretrained discriminator exhibited better performance in all experiments. Experimental results demonstrate that the LAC model achieves higher Bilingual Evaluation Understudy (BLEU) scores and has good potential to augment LRL translations.

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A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Cited by 167 publications

References 82 publications

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

A Novel Multi-Stage Ensemble Model With a Hybrid Genetic Algorithm for Credit Scoring on Imbalanced Data

Machine Translation in Low-Resource Languages by an Adversarial Neural Network

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