A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data

Xu, Zhaozhao; Shen, Derong; Nie, Tiezheng; Kou, Yue

doi:10.1016/j.jbi.2020.103465

Cited by 178 publications

(74 citation statements)

References 41 publications

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“…If the model is trained on an unbalanced dataset, the model may be biased toward the majority class, leading to poor performance [32,33]. One way of solving this problem is to balance the dataset by generating new data similar to the original [34][35][36]. Similarly, BBS motion data can be improved using an oversampling technique [37].…”

Section: Data Augmentation Using the Over-sampling Techniquementioning

confidence: 99%

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Kim

Kyunglim

Jeong

et al. 2021

Sensors

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In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.

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Section: Data Augmentation Using the Over-sampling Techniquementioning

confidence: 99%

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Kim

Kyunglim

Jeong

et al. 2021

Sensors

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“…However, in the early stage, the hybrid algorithms were dominated by the random hybrid sampling algorithm of Seiffert et al (2009) [19]. Later, the improved SMOTE [20].…”

Section: Introductionmentioning

confidence: 99%

An oversampling method for multi-class imbalanced data based on composite weights

et al. 2021

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To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.

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“…The synthetic minority over-sampling technique is an approach to overcome the imbalanced data problem by creating artificial data points that are analogous to the real one. SMOTE is one of the early proposed techniques, which is developed by Chawla in 2002, and used over various domains, such as the medical [59], the industrial [60], and others [61].…”

Section: ) Smotementioning

confidence: 99%

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

Almomani

Qaddoura

Habib³

et al. 2021

IEEE Access

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In recent years, Ransomware has been a critical threat that attacks smartphones. Ransomware is a kind of malware that blocks the mobile's system and prevents the user of the infected device from accessing their data until a ransom is paid. Worldwide, Ransomware attacks have led to serious losses for individuals and stakeholders. However, the dramatic increase of Ransomware families makes to the process of identifying them more challenging due to their continuously evolved characteristics. Traditional malware detection methods (e.g., statistical-based prevention methods) fail to combat the evolving Ransomware since they result in a high percentage of false positives. Indeed, developing a non-classical, intelligent technique to safeguarding against Ransomware is of significant importance. This paper introduces a new methodology for the detection of Ransomware that is depending on an evolutionary-based machine learning approach. The binary particle swarm optimization algorithm is utilized for tuning the hyperparameters of the classification algorithm, as well as performing feature selection. The support vector machines (SVM) algorithm is used alongside the synthetic minority oversampling technique (SMOTE) for classification. The utilized dataset is collected from various sources, which consists of 10,153 Android applications, where 500 of them are Ransomware. The performance of the proposed approach SMOTE-tBPSO-SVM achieved merits over traditional machine learning algorithms by having the highest scores in terms of sensitivity, specificity, and g-mean.

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A hybrid sampling algorithm combining M-SMOTE and ENN based on Random forest for medical imbalanced data

Cited by 178 publications

References 41 publications

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

An oversampling method for multi-class imbalanced data based on composite weights

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

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