This research makes a theoretical contribution by providing straightforward and coherent derivation of logistic model, then estimating the parameters model with fishing data set. The is frequently considered as convenient regression to find associations between binary outcome variable several covariates. also that has numerous practical applications, in banking, engineering, social sciences, me...

The paper aims to find an efficient model for consumer credit scoring using neural networks in comparison with logistic regression. A specific characteristic of the examined data set was that the credit repayment period was not completed. Applicants are divided into three categories: "good", "bad", and indeterminate ("poor") applicants which influenced the model accuracy. Five different modelin...

This paper studies a joint stocking and product offer problem. We have access to a number of products to satisfy the demand over a finite selling horizon. Given that customers choose among the set of offered products according to the multinomial logit model, we need to decide which sets of products to offer over the selling horizon and how many units of each product to stock so as to maximize t...

This work presents a new approach for multi-class pattern recognition based on the hybridization of a linear and nonlinear model. We propose multinomial logistic regression where some new covariates are defined by a product unit neural network, where in turn, the nonlinear basis functions are constructed with the product of the inputs raised to arbitrary powers. The application of this methodol...

Softmax regression, a generalization of Logistic regression (LR) in the setting of multi-class classification, has been widely used in many machine learning applications. However, the performance of softmax regression is extremely sensitive to the presence of noisy data and outliers. To address this issue, we propose a model of robust softmax regression (RoSR) originated from the self-paced lea...