Low Percentage Missing Imputation using KNN, NB and DT
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 101 | Views: 338

Research Paper | Computer Methods in Applied Mechanics and Engineering | Malaysia | Volume 8 Issue 10, October 2019 | Popularity: 6.8 / 10


     

Low Percentage Missing Imputation using KNN, NB and DT

Abdullah Hussein Al-Amoodi


Abstract: The objective of this research is to test data imputation for Missing data over 7 cases. Different machine learning algorithms to impute the missing data were tested and evaluated: K-nearest Neighbor (KNN), Nave Bayes (NB) and Decision Tree (DT). Evaluation was done using t-test for the experiment with different configurations (i. e.5 %, 10 % missing). The result of the experiment shows that KNN has scored better results compared with Nave Bayes and Decision Tree. In conclusion, it is clear that machine learning algorithms can be used for missing data imputation. The implications of this research shows promising potentials for the utilization of KNN


Keywords: Missing Data, Imputation, and Machine Learning Imputation


Edition: Volume 8 Issue 10, October 2019


Pages: 1643 - 1645



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Abdullah Hussein Al-Amoodi, "Low Percentage Missing Imputation using KNN, NB and DT", International Journal of Science and Research (IJSR), Volume 8 Issue 10, October 2019, pp. 1643-1645, https://www.ijsr.net/getabstract.php?paperid=ART20202271, DOI: https://www.doi.org/10.21275/ART20202271

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