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Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Computer Systems Science & Engineering 2022年第7期42卷 303-318页

作者： Ali Algarni Mahmoud Ragab Wardah Alamri samih mmostafa Statistics Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884Egypt Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Educational Technology Department Educational Graduate Studies FacultyKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset’s *** most research studies,the existence of missing values(MVs)is a vital *** addition,any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are *** this paper,the authors propose a novel algorithm for dealing with MVs depending on the feature selec-tion(FS)of similarity classiﬁer with fuzzy entropy *** proposed algo-rithm imputes MVs in cumulative *** candidate feature to be manipulated is selected using similarity classiﬁer with Parkash’s fuzzy entropy *** predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression(BRR)***,any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete *** proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs gener-ated from the three missingness *** evaluation metrics of mean abso-lute error(MAE),root mean square error(RMSE)and coefﬁcient of determination(R2 score)were used to measure the *** results exhibited that perfor-mance vary depending on the size of the dataset,amount of MVs and the missing-ness mechanism ***,compared to other methods,the results showed that the proposed method gives better accuracy and less error in most cases.

关键词： Bayesian ridge regression fuzzy entropy measure feature selection imputation missing values missingness mechanisms similarity classiﬁer medical dataset

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