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https://doi.org/10.17559/TV-20170220135853

Improvement of the Accuracy of Prediction Using Unsupervised Discretization Method: Educational Data Set Case Study

Gabrijela Dimić orcid id orcid.org/0000-0003-4233-540X ; High School of Electrical Engineering and Computer Science, Vojvode Stepe 283, 11000 Belgrade, Serbia
Dejan Rančić ; Faculty of Electronic Engineering, Aleksandra Medvedeva 18, 18000 Niš, Serbia
Ivan Milentijević ; Faculty of Electronic Engineering, Aleksandra Medvedeva 18, 18000 Niš, Serbia
Petar Spalević ; University of Prishtina, Faculty of Technical Sciences, Kneza Miloša 7, 38220 Kosovska Mitrovica, Serbia


Puni tekst: engleski pdf 559 Kb

str. 407-414

preuzimanja: 934

citiraj


Sažetak

This paper presents a comparison of the efficacy of unsupervised and supervised discretization methods for educational data from blended learning environment. Naïve Bayes classifier was trained for each discretized data set and comparative analysis of prediction models was conducted. The research goal was to transform numeric features into maximum independent discrete values with minimum loss of information and reduction of classification error. Proposed unsupervised discretization method was based on the histogram distribution and implementation of oversampling technique. The main contribution of this research is improvement of accuracy prediction using the unsupervised discretization method which reduces the effect of ignoring class feature for educational data set.

Ključne riječi

discretization; data mining; educational data set; entropy; equal width binning; histogram; machine learning; oversampling

Hrčak ID:

199137

URI

https://hrcak.srce.hr/199137

Datum izdavanja:

21.4.2018.

Posjeta: 1.595 *