Logistic Regression Hyperparameter Optimization for Cancer Classification

Arafa, Ahmed; Radad, Marwa; Badawy, Mohammed; El-Fishawy, Nawal

doi:10.21608/mjeer.2021.70512.1034

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…The LR is predictive analysis, as all regression analyses are. Using LR, data can be described and the relationship between a dependent binary variable and one or more independent nominal, ordinal, interval, or ratio-level variables can be explained [14]. The hyperparameters used for the LR method are listed in Table 2.…”

Section: Logistic Regressionmentioning

confidence: 99%

Machine learning based churn analysis for sellers on the e-commerce marketplace

Öztürk,

Tunç,

Akay

2023

International Journal of Mathematics and Computer in Engineering

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The goal of this study is to develop churn models for sellers on the e-commerce marketplace by using machine learning methods. In this sense, three approaches were applied for developing the models. The dataset used in this study includes ten features, which are maturity type, maturity interval, city of the seller, total revenue of the seller, total transaction of the seller, sector type of the seller, business type of the seller, sales channel, installment option and discount type. Random Forest (RF) and Logistic Regression (LR) were used for churn analysis in all of the approaches. In the first approach, models were developed without applying preprocessing operations on the dataset. In the second and third approaches, under sampling and oversampling methods, respectively, were used to balance the data set. By using stratified cross validation on the dataset, F-Scores of the churn models were obtained. The results show that F-Scores were 0.76, 0.71 and 0.92 for the three approaches developed with RF, and 0.84, 0.68 and 0.69 for the three approaches developed with LR, respectively.

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Section: Logistic Regressionmentioning

confidence: 99%

Machine learning based churn analysis for sellers on the e-commerce marketplace

Öztürk,

Tunç,

Akay

2023

International Journal of Mathematics and Computer in Engineering

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“…However, after optimization, it reaches 96.50% to 98.77% with a 0.012% error. Ahmed Arafa et al [12] determine the best hyperparameters for a Logistic regression model using grid search, random search, Bayesian Tree Parzen Estimator (TPE) and Simulated Annealing (SA) optimization techniques. The authors used the Wisconsin Breast Cancer Dataset (WBCD) with 569 instances(Wolberg et al [1]).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Convergence of Optimization Algorithms for Logistic Regression: A Breast Cancer Diagnosis Application

Borbor,

Herrera,

Ashraf

et al. 2024

Preprint

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This paper explores strategies to enhance the convergence of Newton and Trust Region methods, which are employed to obtain optimal solutions for the optimization problems underlying the Logistic Regression technique. The optimization problems consist of maximizing the likelihood function and minimizing the sum of squared errors. These are addressed using the mentioned gradient-based algorithms. Improvements include using a local search heuristic to find a good initial solution and determining the optimal learning rate at each iteration. The heuristic-generated initial points significantly improve the convergence rate of these gradient-based methods. Additionally, instead of using a fixed learning rate or step size, the optimization process dynamically computes these parameters using the Golden Search method, ensuring a more adaptive and efficient search strategy. Extensive numerical experiments and a detailed case study validate the effectiveness of this approach.

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