Neural Network and Data Envelopment Analysis to solve binary Classification Problems
Publish place: The 7th International Conference on Economics and Management
Publish Year: 1395
Type: Conference paper
Language: English
View: 662
This Paper With 22 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
Export:
Document National Code:
ICOEM01_067
Index date: 15 December 2016
Neural Network and Data Envelopment Analysis to solve binary Classification Problems abstract
In this paper using additive model in Data Envelopment Analysis (DEA) technique, a methodfor solving separable linear and nonlinear classification problems presented. This method willresult an accurate classification for solving binary classification problems with linearlyseparable classes. One key feature of this method is that in regards of using additive DEAmodel there is no need for positive data and thus it has less computational complexity. Notedthat, for solving separable nonlinear binary classification, using Gaussian basic functions, anew RBFN artificial neural network will be presented. In corresponding section of supervisor,instead of using a linear function, a procedure as additive model in DEA has been utilizedwhich is more stable with higher speed in successive iterations. Furthermore, two examplesalso used to demonstrate the validity of the presented method.
Neural Network and Data Envelopment Analysis to solve binary Classification Problems Keywords:
Neural Network and Data Envelopment Analysis to solve binary Classification Problems authors
Mohsen Vaez-Ghasemi
Department of Mathematics, Rasht Branch, Islamic Azad University
Zohreh Moghaddas
Department of Mathematics, Qazvin Branch, Islamic Azad University
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :