On the basis of the existing stepwise logistic regression model, by adding additional conditions restricting the training of the logistic regression model to fall into over fitting, the particle swarm ...

Feature selection algorithm could select the relative attributes, but it is possible that some important features are deleted. In this paper, we propose hierarchical clustering based logistic ...

algorithm. Unlike conventional gradient-based methods, which suffer from vanishing gradients and inefficient training, our proposed approach can effectively minimize squared loss and logistic loss. To ...

Study objective: In social epidemiology, it is easy to compute and interpret measures of variation in multilevel linear regression, but technical difficulties exist in the case of logistic regression.

As one of the important statistical methods, quantile regression(QR) extends traditional regression ... objective and develop a distributed primal-dual hybrid gradient (dPDHG) algorithm for this ...

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This study presents a comprehensive machine learning approach to predict oil well productivity decline. Using advanced algorithms and feature engineering, we developed predictive models that can ...