Feature selection for multiple water quality status: Integrated bootstrapping and SMOTE approach in imbalance classes

Uyun, Shofwatul; Sulistyowati, Eka

doi:10.11591/ijece.v10i4.pp4331-4339

Cited by 13 publications

(14 citation statements)

References 24 publications

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“…In [6], the Author used SMOTE and boostrapping to handle unbalanced data. They used several feature selection methods with decision tree, k-NN and Bayes classification model.…”

Section: Introductionmentioning

confidence: 99%

Oversampling technique in student performance classification from engineering course

Rachburee

Punlumjeak

2021

IJECE

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<span>The first year of an engineering student was important to take proper academic planning. All subjects in the first year were essential for an engineering basis. Student performance prediction helped academics improve their performance better. Students checked performance by themselves. If they were aware that their performance are low, then they could make some improvement for their better performance. This research focused on combining the oversampling minority class data with various kinds of classifier models. Oversampling techniques were SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN and four classifiers were applied using MLP, gradient boosting, AdaBoost and random forest in this research. The results represented that Borderline-SMOTE gave the best result for minority class prediction with several classifiers.</span>

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“…In [6], the Author used SMOTE and boostrapping to handle unbalanced data. They used several feature selection methods with decision tree, k-NN and Bayes classification model.…”

Section: Introductionmentioning

confidence: 99%

Oversampling technique in student performance classification from engineering course

Rachburee

Punlumjeak

2021

IJECE

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“…This paper proposes and conducts a feature selection because the classifier can provide better classification results [8]. t-distributed stochastic neighbor embedding (t-SNE) is a non-linear technique to map multidimensional data to lower dimensional space [9].…”

Section: Feature Selectionmentioning

confidence: 99%

Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

Nguyễn

Doan

Ngoc

et al. 2022

IJECE

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<span>In this paper, we introduce a mobile application called CarSafe, in which data from the acceleration sensor integrated on smartphones is exploited to come up with an efficient classification algorithm. Two statuses, "Driving" or "Not driving," are monitored in the real-time manner. It enables automatic actions to help the driver safer. Also, from these data, our software can detect the crash situation. The software will then automatically send messages with the user's location to their emergency departments for timely assistance. The application will also issue the same alert if it detects a driver of a vehicle driving too long. The algorithm's quality is assessed through an average accuracy of 96.5%, which is better than the previous work (i.e., 93%).</span>

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“…The reason why feature selection is effective in dealing with overlapping is because of its ability to eliminate uninformative predictors and reduce dimensionality of feature space [8]. However, on the other hand with the noise, the performance given by feature selection can decrease [9] and noise basically has an influence on classification performance [10]. Noise handling in general uses the method of resampling, but often encounters obstacles if there is a state of overlapping [6].…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

Ongko

Hartono

2021

Bulletin EEI

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Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.

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Feature selection for multiple water quality status: Integrated bootstrapping and SMOTE approach in imbalance classes

Cited by 13 publications

References 24 publications

Oversampling technique in student performance classification from engineering course

Oversampling technique in student performance classification from engineering course

Develop algorithms to determine the status of car drivers using built-in accelerometer and GBDT

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

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