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- W2994233886 abstract "In the analyzed students’ educational data several parameters such as True Postive Rate, False Positive Rate and Classification Error were used as a yard stick in measuring the performance of both Kstar and BayeNet algorithms in mining the educational data. The performance investigation of the applied classifiers revealed hidden knowledge in the data set which was helpful in the re-calibration of the model to yield a higher precision of each of the classifier with minimal classification error. Keywords: Data Mining, Educational Data Mining, Knowledge Discovery, Student, Classifiers, Performance, Investigation." @default.
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- W2994233886 date "2015-01-01" @default.
- W2994233886 modified "2023-09-25" @default.
- W2994233886 title "Investigating the Performance of Selected Weka Classifiers for Knowledge Discovery in Mining Educational Data" @default.
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