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Calculating different weights in feature values in logistic regression

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Chang-Hwan LeeAuthors Info & Claims

ICCIP '16: Proceedings of the 2nd International Conference on Communication and Information Processing

Pages 148 - 150

https://doi.org/10.1145/3018009.3018017

Published: 26 November 2016 Publication History

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Abstract

In traditional logistic regression model, every value of feature has the same weight. In this paper, we propose a new weighting method for logistic regression, which assigns a different weight to each feature value. A gradient approach is used to calculate the optimal weights of feature values.

References

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David W. Hosmer, Stanley Lemeshow, and Rodney X. Sturdivant. Applied logistic regression,.John Wiley & Sons, 2013.

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Index Terms

Calculating different weights in feature values in logistic regression
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Information & Contributors

Information

Published In

ICCIP '16: Proceedings of the 2nd International Conference on Communication and Information Processing

November 2016

272 pages

ISBN:9781450348195

DOI:10.1145/3018009

Conference Chairs:
Maode Ma
Nanyang Technological University, Singapore
,
Jalel Ben-Othman
University of Paris 13, France
,
Feng Gang
UESTC, China
,
Program Chairs:
Masahiko Ooki
Nishinippon Institute of Technology, Japan
,
Gihwan Cho
Jeonbuk National University, Korea

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2016

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Korea government

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ICCIP '16

ICCIP '16: 2nd International Conference on Communication and Information Processing

November 26 - 29, 2016

Singapore, Singapore

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Overall Acceptance Rate 61 of 301 submissions, 20%

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