A threshold varying bisection method for cost sensitive learning in neural networks

Parag C. Pendharkar

doi:10.1109/IJCNN.2005.1555996

A threshold varying bisection method for cost sensitive learning in neural networks

Parag C. Pendharkar

School of Business Administration (Harrisburg)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network learning. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	1039-1044
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1555996
State	Published - Dec 1 2005
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: Jul 31 2005 → Aug 4 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2

Other

Other	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	7/31/05 → 8/4/05

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2005.1555996

Cite this

@inproceedings{81310932300c4992998e5fda6c37eb23,

title = "A threshold varying bisection method for cost sensitive learning in neural networks",

abstract = "We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network learning. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method.",

author = "Pendharkar, {Parag C.}",

year = "2005",

month = dec,

day = "1",

doi = "10.1109/IJCNN.2005.1555996",

language = "English (US)",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "1039--1044",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "International Joint Conference on Neural Networks, IJCNN 2005 ; Conference date: 31-07-2005 Through 04-08-2005",

}

Pendharkar, PC 2005, A threshold varying bisection method for cost sensitive learning in neural networks. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1555996, Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1039-1044, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 7/31/05. https://doi.org/10.1109/IJCNN.2005.1555996

A threshold varying bisection method for cost sensitive learning in neural networks. / Pendharkar, Parag C.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 1039-1044 1555996 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - We propose a bisection method for varying classification threshold value for cost sensitive neural network learning. Using simulated data and different cost asymmetries, we test the proposed threshold varying bisection method and compare it with the traditional fixed-threshold method based neural network learning. The results of our experiments illustrate that the proposed threshold varying bisection method performs better than the traditional fixed-threshold method.

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A threshold varying bisection method for cost sensitive learning in neural networks

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