Classification using nonparametric logistic regression for predicting working status

Wahyu Wibowo*, Ralmii Amelia, Fanny Ayu Octavia, Regina Niken Wilantari

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Logistic regression is classical and prominent method for classification and it is used as benchmark for comparing the alternative methods. However, logistic regression is not always superior compared to the other methods. The accuracy of logistic regression could be improved by incorporating nonparametric model. The response variable used in this study is working status of housewife thajt categorized as working or not-working. Meanwhile the predictor variables consists of three variables, they are highest education level, age. and household expenditure. The result of fitting model shows that by incorporating nonparametric model to the binary logistic regression model can improve the classification accuracy. This is indicated not only by accuracy percentage, but also by area under Receiving Operating Characteristic (ROC) curve. The dataset will be divided into two parts, 80% as training data and 20% as testing data. The classification accuracy resulted by the binary logistic regression model is 60.36% for training data and 59.30% for testing data. Meanwhile, the classification accuracy of nonparametric logistic model is 63.43% for training data and 64.94%. for testing data. The classification accuracy and area under curve of nonparametric logistic regression is higher than those of binary logistic regression.

Original languageEnglish
Title of host publicationInternational Conference on Mathematics, Computational Sciences and Statistics 2020
EditorsCicik Alfiniyah, Fatmawati, Windarto
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440739
DOIs
Publication statusPublished - 26 Feb 2021
EventInternational Conference on Mathematics, Computational Sciences and Statistics 2020, ICoMCoS 2020 - Surabaya, Indonesia
Duration: 29 Sept 2020 → …

Publication series

NameAIP Conference Proceedings
Volume2329
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Conference on Mathematics, Computational Sciences and Statistics 2020, ICoMCoS 2020
Country/TerritoryIndonesia
CitySurabaya
Period29/09/20 → …

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