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Detail Karya Ilmiah Dosen

Septian Rahardiantoro, Anang Kurnia, Mulyanto Raharjo, Yusma Yanti

Judul : An alternative approach in predictive modeling using model averaging scheme for logistic regression case (case study: application in class prediction of autistic spectrum disorder data)

Abstrak :
Logistic regression has become a popular method for handling predictive modeling when the response variable has a categorical scale. The difference in category proportion in response variable could influence the prediction accuracy. This research applied the model averaging approach for logistic regression in purpose to improve the prediction accuracy in different proportion of each category. Model averaging has the idea to combine some model candidates based on the specified weight to be the final model. The model candidate in model averaging generated based on all possibilities variable selection in the model. AIC weight is chosen to apply in the combination of all possible model candidates. It is illustrated with an application to data from a classification of Autistic Spectrum Disorder data. The result of this case indicated that the logistic model averaging had better performances.

Tahun : 2019 Media Publikasi : Jurnal Internasional

Kategori : Prosiding No/Vol/Tahun : 012039 / 299 / 2019

ISSN/ISBN : IOP Conf. Series: Earth and Environmental Science2

PTN/S : Institut pertanian Bogor dan Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :

Myers RH. 1990. Classical and Modern Regression with Applications (Second Edition). Boston: PWS-Kent Publising Company

Ghosh D, Yuan Z. 2009. An Improved Model Averaging Scheme for Logistic Regression. J Multivar Anal. 100(8): 1670–1681.

Burnham KP, Anderson DR. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York: Springer-Verlag

Tabtah F. 2017. Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp.1-6. Taichung City, Taiwan, ACM.

Perrone MP. 1993. Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization [dissertation]. Providence(US): Brown University.

Claeskens G, Hjort NL. 2008. Model Selection and Model Averaging. New York (US): Cambridge University Press.

Kuhn M. 2008. Building Predictive Models in R Using The caret Package. Journal of Statistical Software

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Detail Karya Ilmiah Dosen

Septian Rahardiantoro, Anang Kurnia, Mulyanto Raharjo, Yusma Yanti

Judul	:	An alternative approach in predictive modeling using model averaging scheme for logistic regression case (case study: application in class prediction of autistic spectrum disorder data)
Abstrak	:	Logistic regression has become a popular method for handling predictive modeling when the response variable has a categorical scale. The difference in category proportion in response variable could influence the prediction accuracy. This research applied the model averaging approach for logistic regression in purpose to improve the prediction accuracy in different proportion of each category. Model averaging has the idea to combine some model candidates based on the specified weight to be the final model. The model candidate in model averaging generated based on all possibilities variable selection in the model. AIC weight is chosen to apply in the combination of all possible model candidates. It is illustrated with an application to data from a classification of Autistic Spectrum Disorder data. The result of this case indicated that the logistic model averaging had better performances.
Tahun	:	2019	Media Publikasi	:	Jurnal Internasional
Kategori	:	Prosiding	No/Vol/Tahun	:	012039 / 299 / 2019
ISSN/ISBN	:	IOP Conf. Series: Earth and Environmental Science2
PTN/S	:	Institut pertanian Bogor dan Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	Myers RH. 1990. Classical and Modern Regression with Applications (Second Edition). Boston: PWS-Kent Publising Company Ghosh D, Yuan Z. 2009. An Improved Model Averaging Scheme for Logistic Regression. J Multivar Anal. 100(8): 1670–1681. Burnham KP, Anderson DR. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York: Springer-Verlag Tabtah F. 2017. Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp.1-6. Taichung City, Taiwan, ACM. Perrone MP. 1993. Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization [dissertation]. Providence(US): Brown University. Claeskens G, Hjort NL. 2008. Model Selection and Model Averaging. New York (US): Cambridge University Press. Kuhn M. 2008. Building Predictive Models in R Using The caret Package. Journal of Statistical Software
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