Performance improvement of logistic regression for binary classification by Gauss-Newton method

Jamhuri, Mohammad, Mukhlash, Imam and Irawan, Mohammad Isa (2022) Performance improvement of logistic regression for binary classification by Gauss-Newton method. Presented at The 2022 5th International Conference on Mathematics and Statistics (ICoMS 2022), 17-19 Jun 2022, Paris, France.

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Abstract

This paper proposes a new approach to optimizing cost function for binary logistic regression by the Gauss-Newton method. This method was applied to the backpropagation phase as a part of the training process to update the weighted coefficients. To show the performance of the approach, we used two data sets to train the logistic regression model for binary classification problems. Our experiment demonstrated that the proposed methods could perform better than gradient descent for both examples, as we expected. Furthermore, the performance of our approach is more advanced than the classical method, either in speed or accuracy.

Item Type:	Conference (Paper)
Subjects:	01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified 01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational mathematics
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Mr Jamhuri Mohammad
Date Deposited:	15 Jun 2023 08:13

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