Efficient Pre-Processing Techniques for Improving Classifiers Performance

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dc.contributor.authorNickolas, S
dc.contributor.authorShobha, K
dc.date.accessioned2026-11-11T05:47:29Z
dc.date.available2026-11-11T05:47:29Z
dc.date.issued2021-12-30
dc.description.abstractData pre-processing plays a vital role in the life cycle of data mining for accomplishing quality outcomes. In this paper, it is experimentally shown the importance of data pre-processing to achieve highly accurate classifier outcomes by imputing missing values using a novel imputation method, CLUSTPRO, by selecting highly correlated features using Correlation-based Variable Selection (CVS) and by handling imbalanced data using Synthetic Minority Over-sampling Technique (SMOTE). The proposed CLUSTPRO method makes use of Random Forest (RF) and Expectation Maximization (EM) algorithms to impute missing. The imputed results are evaluated using standard evaluation metrics. The CLUSTPRO imputation method outperforms existing, state-of-the-art imputation methods. The combined approach of imputation, feature selection, and imbalanced data handling techniques has significantly contributed to attaining an improved classification accuracy (AUC curve) of 40%–50% in comparison with results obtained without any pre-processing.
dc.identifier.issn15409589
dc.identifier.urihttps://doi.org/10.13052/jwe1540-9589.2124
dc.identifier.urihttp://nitt.ndl.gov.in/handle/123456789/429
dc.language.isoen
dc.publisherRiver Publishers
dc.relation.ispartofseriesJournal of Web Engineering; Vol. 21/ No. 2
dc.titleEfficient Pre-Processing Techniques for Improving Classifiers Performance
dc.typeArticle
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