ADASYN and ABC-optimized RBF convergence network for classification of electroencephalograph signal

Satapathy, Sandeep Kumar; Mishra, Shruti; Mallick, Pradeep Kumar; Chae, Gyoo‐Soo

doi:10.1007/s00779-021-01533-4

Cited by 21 publications

(14 citation statements)

References 33 publications

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“…The analyzed dataset is highly unbalanced as the ratio is 12:1. SMOTE 42 and ADASYN 43 , 44 are applied to the training datasets. However, considering the large training dataset, oversampling considerably increases the computational time.…”

Section: Resultsmentioning

confidence: 99%

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning

Oprea

Bârã

2022

Sci Rep

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Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper, we obtain meaningful insights from fraud detection using real datasets of Tunisian electricity consumption metered by conventional meters. We propose an extensive feature engineering approach using the structured query language (SQL) analytic functions. Furthermore, double merging of datasets reveals more dimensions of the data allowing better detection of irregularities in consumption. We analyze the results of several machine learning (ML) algorithms that manage cases of weakly correlated features and highly unbalanced datasets. The skewness of the target is approached as a regular characteristic of the input data because most of consumers are fair and only a small portion attempt to mislead the utility companies by tampering with metering devices. Our fraud detection solutions consist of combining classifiers with an anomaly detection feature obtained with an unsupervised ML algorithm—Isolation Forest, and extensive feature engineering using SQL analytic functions on large datasets. Several techniques for feature processing enhanced the Area Under the Curve score for Decision Tree algorithm from 0.68 to 0.99.

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Section: Resultsmentioning

confidence: 99%

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning

Oprea

Bârã

2022

Sci Rep

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“…Different from the SMOTE algorithm which generates the equal number of synthetic samples for every minority class data example, the key idea of the ADASYN [26][27][28] algorithm is to determine how many new samples should be resampled for every minority class sample automatically, by using density distribution as the standard. The dataset generated by the ADASYN algorithm will not only show the balanced distribution of data, but also require the classification algorithm to devote more attention to those samples that are hard to learn [27].…”

Section: Processing Methods Of Unbalanced Datasetmentioning

confidence: 99%

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

et al. 2022

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Coronary artery disease (CAD) is one of the diseases with the highest morbidity and mortality in the world. In 2019, the number of deaths caused by CAD reached 9.14 million. The detection and treatment of CAD in the early stage is crucial to save lives and improve prognosis. Therefore, the purpose of this research is to develop a machine-learning system that can be used to help diagnose CAD accurately in the early stage. In this paper, two classical ensemble learning algorithms, namely, XGBoost algorithm and Random Forest algorithm, were used as the classification model. In order to improve the classification accuracy and performance of the model, we applied four feature processing techniques to process features respectively. In addition, synthetic minority oversampling technology (SMOTE) and adaptive synthetic (ADASYN) were used to balance the dataset, which included 71.29% CAD samples and 28.71% normal samples. The four feature processing technologies improved the performance of the classification models in terms of classification accuracy, precision, recall, F1 score and specificity. In particular, the XGBboost algorithm achieved the best prediction performance results on the dataset processed by feature construction and the SMOTE method. The best classification accuracy, recall, specificity, precision, F1 score and AUC were 94.7%, 96.1%, 93.2%, 93.4%, 94.6% and 98.0%, respectively. The experimental results prove that the proposed method can accurately and reliably identify CAD patients from suspicious patients in the early stage and can be used by medical staff for auxiliary diagnosis.

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“…The basic idea of ADASYN is to use weighted distributions for different minority classes of samples based on their difficulty of learning; this generates more synthetic data for the minority samples that are more difficult to learn 30 . This ADASYN learning mechanism has led to a wide range of applications in different fields 31,32 . In summary, although the abovementioned improved oversampling method based on SMOTE enables the synthesis of new samples and achieves good classification results in some specific cases, there are still some associated limitation.…”

Section: Related Workmentioning

confidence: 99%

SD-KMSMOTE: oversampling method for imbalanced data based on spatial distribution of minority samples

Yang

Pan

Zhang

2022

Preprint

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With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling methods randomly sample or sample only for a particular area, which affects the classification results. To solve the above limitations, this study proposes an imbalanced data oversampling method, SD-KMSMOTE, based on the spatial distribution of minority samples. A filter noise pre-treatment is added, the category information of the near-neighbouring samples is considered, and the existing minority class sample noise is removed. These conditions lead to the design of a new sample synthesis method, and the rules for calculating the weight values are constructed on this basis. The spatial distribution of minority class samples is considered comprehensively; they are clustered, and the sub-clusters that contain useful information are assigned larger weight values and more synthetic sample numbers. The experimental results show that the experimental results outperforms existing methods in terms of precision, recall, F1 score, G-mean, and area under the curve values when the proposed method is used to expand the imbalanced dataset in the field of medicine and other fields.

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ADASYN and ABC-optimized RBF convergence network for classification of electroencephalograph signal

Cited by 21 publications

References 33 publications

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning

Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

SD-KMSMOTE: oversampling method for imbalanced data based on spatial distribution of minority samples

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