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- W3035303393 abstract "In the past few years’ classification has undergone some major evolution. With constant surge of the amount of data gathered from different sources efficient processing and analysis of data is becoming difficult. Due to the uneven distribution of data among classes data classification with machine learning techniques has become more tedious. While most algorithms focus on major data samples they ignore the minor class data. Thus data skewing issue is one of the critical problems that need attention of researchers. The paper stresses upon data preprocessing using sampling techniques to overcome the data skewing problem. Here three different sampling techniques such as Resampling, SpreadSubSampling and SMOTE are implemented to reduce this uneven data distribution issue and classified with K-Nearest Neighbour algorithm. The performance of classification is evaluated with various performance metrics to determine the efficiency of classification." @default.
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- W3035303393 date "2020-07-16" @default.
- W3035303393 modified "2023-10-05" @default.
- W3035303393 title "Optimization of Skewed Data Using Sampling-Based Preprocessing Approach" @default.
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- W3035303393 doi "https://doi.org/10.3389/fpubh.2020.00274" @default.
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