Imbalanced data classifier by using ensemble fuzzy c-means clustering

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dc.contributor.authorKocyigit Y.
dc.contributor.authorSeker H.
dc.date.accessioned2024-07-22T08:19:25Z
dc.date.available2024-07-22T08:19:25Z
dc.date.issued2012
dc.description.abstractPattern classifiers developed with the imbalanced data set tend to classify an object to the class with the highest number of samples, resulting in higher overall classifier accuracy but lower sensitivity. A new approach based on a dynamic under-sampling procedure is therefore proposed to improve the classification of imbalanced datasets that are quite common in bio-medicine. To overcome a class imbalance, the dataset is resampled by using the ensemble fuzzy c-means clustering method. The under-sampling procedure is then applied to the majority class to balance the size of the classes. Compared to the existing classifiers, the proposed method yields not only higher classification accuracy and sensitivity but also more stable classification performance under different data sets, classifiers and their parameters, indicating that it is independent of particular clustering or classification methods. © 2012 IEEE.
dc.identifier.DOI-ID10.1109/BHI.2012.6211746
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/17671
dc.language.isoEnglish
dc.subjectBiomedical equipment
dc.subjectBiosensors
dc.subjectFuzzy systems
dc.subjectClass imbalance
dc.subjectClassification accuracy
dc.subjectClassification methods
dc.subjectClassification performance
dc.subjectData sets
dc.subjectFuzzy C means clustering
dc.subjectFuzzy c-means clustering method
dc.subjectImbalanced data
dc.subjectImbalanced Data-sets
dc.subjectNumber of samples
dc.subjectPattern classifier
dc.subjectUnder-sampling
dc.subjectClassification (of information)
dc.titleImbalanced data classifier by using ensemble fuzzy c-means clustering
dc.typeConference paper

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