Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Zhang, Zhong-Liang; Krawczyk, Bartosz; García, Salvador; Rosales-Pérez, Alejandro; Herrera, Francisco

doi:10.1016/j.knosys.2016.05.048

Cited by 95 publications

(32 citation statements)

References 52 publications

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“…Future research will summarize general relations between algorithms performance and other attributes like attributes' number and samples' cardinality. Multiclass imbalance cases [43] are also considered in the later mining tasks.…”

Section: Discussionmentioning

confidence: 99%

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Wang

Luo

Huang

et al. 2017

Computational Intelligence and Neuroscience

117

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Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

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Section: Discussionmentioning

confidence: 99%

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Wang

Luo

Huang

et al. 2017

Computational Intelligence and Neuroscience

117

View full text Add to dashboard Cite

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“…As a preliminary exploration of the more challenging multiclass imbalance data setting [60], we chose the waveform data simulation from the mlbench R-package [55], which produces three classes of (approximately) equal size. We generated N = 1000 samples for the training (initially) and test data sets.…”

Section: Waveform Simulationsmentioning

confidence: 99%

“…However, the ratio of competent to non-competent classifiers becomes 1:1 for data with four classes and monotonically decreases in favor of non-competent classifiers as the number of classes increases. In these imbalanced multiclass settings, a more sophisticated approach using some form of weighted voting should be used instead [60,62].…”

Section: Cassini Simulationsmentioning

confidence: 99%

A random forests quantile classifier for class imbalanced data

O’Brien

Ishwaran

2019

Pattern Recognition

129

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Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a densitybased approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to tt-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.

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“…Pada tahap prepossessing data mining membutuhkan ketelitian (Batra & Sachdev, 2016), yang selanjutnya akan dilakukan proses kalsifikasi dan clustering (Usharani & P.Sammulal, 2016). Machine learning dan data mining memiliki kumpulan data yang terbagi menjadi satu atau lebih dari satu kelas, dan sering terjadi ketidakseimbangan kelas (Imbalanced) (Zhang, Krawczyk, Garcìa, Rosales-Pérez, & Herrera, 2016). Tujuan dalam pengklasifikasian dataset memberikan prediksi terhadap data yang diujikan dan tidak mengenal imbalanced data.…”

Section: Pendahuluanunclassified

Penanganan imbalance class data laboratorium kesehatan dengan Majority Weighted Minority Oversampling Technique

Untoro

Buliali

2018

register. jurnal. ilm. teknologi. sistem. inf.

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Diagnosis suatu penyakit akan menjadi tepat jika didukung dengan berbagai proses mulai pengecekan awal (amannesa) sampai pengecekan laboratorium. Hasil dari proses laboratorium mempunyai informasi berbagai penyakit, akan tetapi beberapa jenis penyakit memiliki prevalensi rendah. Penyakit bervalensi rendah memiliki pengaruh dalam penanganan pasien lebih lanjut. Dengan rasio yang tidak seimbang data laboratorium akan menyebabkan nilai akurasi menjadi rendah dalam pengklasifikasian dan penanganan penyakit. Majority Weighted Minority Oversampling Technique (MWMOTE) adalah saalah satu cara untuk menyelesaikan imbalanced. Penelitian ini bertujuan menangani permasalahan ketidakseimbangan data laboratorium kesehatan sehingga diperoleh hasil pengklasifikasian penyakit dengan tingkat akurasi lebih tinggi. Hasil pada penelitian ini menunjukkan bahwa MWMOTE dapat meningkatkan akurasi untuk permasalahan ketidakseimbangan data sebesar 3,13%. Diagnosis of a disease will be appropriate if supported by various processes ranging from initial checks (amannesa) to laboratory checks. Results from the laboratory process have information on various diseases, but some types of diseases have a low prevalence. Low-valvature disease has an effect in the treatment of the patient further. With an unbalanced ratio the laboratory data will cause the accuracy value to be low in the classification and handling of the disease. Majority Weighted Minority Oversampling Technique (MWMOTE) is one way to complete imbalanced. This study aims to address the problem of imbalance of health laboratory data to obtain the results of the classification of disease with a higher degree of accuracy. The results of this study indicate that MWMOTE can improve accuracy for data imbalance problems by 3.13%.

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Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Cited by 95 publications

References 52 publications

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

A random forests quantile classifier for class imbalanced data

Penanganan imbalance class data laboratorium kesehatan dengan Majority Weighted Minority Oversampling Technique

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