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FG-smote:Fuzzy-based gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 269-286页

作者： Putta Hemalatha Geetha Mary Amalanathan SCOPE Vellore Institute of TechnologyVelloreIndia

Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding *** data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a *** issue is known as the imbalance problem,which is one of the most common issues occurring in real-time *** of imbalanced datasets is a ubiquitous challenge in the field of data *** data degrades the performance of the classifier by producing inaccurate ***/methodology/approach-In the proposed work,a novel fuzzy-based gaussian synthetic minority oversampling(FG-smote)algorithm is proposed to process the imbalanced *** mechanism of the gaussian smote technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class *** ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure ***-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based gaussian smote technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion *** limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-smote to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed ***/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced ***-smote has aided in balancing minority and majority class datasets.

关键词： Imbalanced data gaussian smote Levenshtein distance measure technique Skewed class distribution Fuzzy based gaussian smote Deep learning Deep belief network classifie

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