Hybrid Methods for Class Imbalance Learning Employing Bagging with Sampling Techniques

Ahmed, Sajid; Mahbub, Asif; Rayhan, Farshid; Jani, Md. Rafsan; Shatabda, Swakkhar; Farid, Dewan Md.

doi:10.1109/csitss.2017.8447799

Cited by 38 publications

(15 citation statements)

References 25 publications

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“…Some other related works based on ensemble classification and sampling methods also contribute to resolving imbalanced classification. For example, Ahmed [ 68 ] applied hybrid sampling in the RSYNBagging classifier, which considered the diversification of imbalanced data. Additionally, the ADASYNBagging algorithm [ 69 ] was coined by incorporating an algorithm and over-sampling.…”

Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Ensemble Approaches For Imbalanced Classificationmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…When this occurs, a large number of majority cases will be misclassified. However, despite these drawbacks, RUS generally works better than other under-sampling methods [11].…”

Section: Random Under-samplingmentioning

confidence: 98%

Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data

Malek

Yaacob

Wah

et al. 2022

IJEECS

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Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.

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“…Dalam perkembangannya, metode klasifikasi sudah sampai di mana terdapat teknik yang menggabungkan lebih dari satu metode klasifikasi untuk membentuk sebuah algoritme klasifikasi yang lebih kuat. Teknik tersebut adalah ensemble method, ensemble method mengombinasikan lebih dari satu metode klasifikasi dasar dengan tujuan menghasilkan sebuah model yang lebih akurat dibanding dengan metode klasifikasi secara individu [19]. Terdapat beberapa model ensemble method yang popular digunakan pada penelitian-penelitian terkait klasifikasi di antaranya, Stacking, Bagging (Bootstrap Aggregation), dan Boosting.…”

Section: Klasifikasiunclassified

Seleksi Fitur dan Penanganan Imbalanced Data menggunakan RFECV dan ADASYN

Pratama¹,

Chandra²,

Presetyaningrum³

2022

eksplora

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Proses data mining bekerja terhadap data yang tersedia. Jika dataset tidak tersedia sepenuhnya, hasil pengolahan data mining menjadi tidak optimal. Terdapat beberapa kondisi data yang perlu penanganan terlebih dahulu sebelum memasuki tahap data mining. Salah satunya ialah imbalanced class yang merupakan kondisi di mana distribusi data pada setiap kelas tidak proporsional. Sebagai salah satu cara untuk efisiensi proses klasifikasi, seleksi fitur dapat memenuhi kebutuhan tersebut karena hasil dari seleksi fitur adalah sebuah dataset dengan jumlah atribut yang lebih sedikit dari sebelumnya. Untuk menyelesaikan permasalahan imbalanced class, ADASYN digunakan dalam penelitian ini sebagai metode untuk menyeimbangkan proporsi kelas pada dataset. Sedangkan RFECV digunakan sebagai metode fitur seleksi yang dapat meningkatkan efisiensi pada proses klasifikasi. Setelah dilakukan evaluasi dari hasil klasifikasi pada dataset yang menggunakan seleksi fitur, didapatkan hasil klasifikasi yang lebih baik dibandingkan dengan hasil klasifikasi pada dataset tanpa seleksi fitur. Hal tersebut dibuktikan dengan perbandingan antara hasil terbaik dari akurasi klasifikasi dataset tanpa seleksi fitur. Hasil dari metode CART sebesar 85.1% yang merupakan hasil dari pengolahan data tanpa menggunakan metode fitur seleksi. sedangkan metode Bagging k-NN yang menghasilkan akurasi sebesar 88% yang di aplikasikan pada dataset dengan seleksi fitur. Sehingga dapat disimpulkan bahwa seleksi fitur dapat meningkatkan akurasi pada klasifikasi.

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Hybrid Methods for Class Imbalance Learning Employing Bagging with Sampling Techniques

Cited by 38 publications

References 25 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data

Seleksi Fitur dan Penanganan Imbalanced Data menggunakan RFECV dan ADASYN

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