Performance analysis of one-dimensional Naïve bayes as a data imputation method for car insurance problems

Natalia Aji Yuwanti, Hendri Murfi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Machine learning methods are very widely used in helping human work. Not all data is as we expected. Some data have missing values. Data that has a missing value must be handled first at the pre-processing stage, one of which is by the imputation of the missing value. This study is comparing the imputation method of missing value uses mode and One-Dimensional Naïve Bayes Classifier (1DNBC) to determine the performance analysis by using Support Vector Machine (SVM) for the prediction of car insurance participation. A better method is seen from the accuracy. Based on the simulation is obtained the same results for imputation using mode and One-Dimensional Naïve Bayes are 1.00, which when examined further turns out to be the imputation of each missing value with the mode and prediction of imputation with One-Dimensional Naïve Bayes are the same.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2020
EditorsBudi Purnama, Dewanta Arya Nugraha, Fuad Anwar
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440302
DOIs
Publication statusPublished - 16 Nov 2020
Event2020 International Conference on Science and Applied Science, ICSAS 2020 - Surakarta, Indonesia
Duration: 7 Jul 2020 → …

Publication series

NameAIP Conference Proceedings
Volume2296
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2020 International Conference on Science and Applied Science, ICSAS 2020
CountryIndonesia
CitySurakarta
Period7/07/20 → …

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