A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO
Amir Eshaghi Chaleshtori
TL;DR
The paper addresses price prediction in high-dimensional settings by integrating Elastic Net with multi-objective optimization to select informative features. It introduces a novel decision-level fusion that combines normalized $R_{adj}^{2}$ weights, ESS, and SAW to produce a stable final feature subset from Pareto fronts. Across two real price datasets, the proposed fusion method improves predictive accuracy and reduces feature cardinality compared with Elastic Net and GA-based benchmarks, as evidenced by lower $RMSE_{cv}$ and higher $R_{adj}^{2}$. This fusion framework enhances practical price-prediction pipelines by delivering robust, scalable feature selection and decision fusion for high-dimensional regression tasks.
Abstract
Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics, including government rules, international transactions, and speculation and expectation. As the dependent variable in price prediction, it is affected by several independent and correlated variables which may challenge the price prediction. To overcome this challenge, machine learning algorithms allow more accurate price prediction without explicitly modeling the relatedness between variables. However, as inputs increase, it challenges the existing machine learning approaches regarding computing efficiency and prediction effectiveness. Hence, this study introduces a novel decision level fusion approach to select informative variables in price prediction. The suggested metaheuristic algorithm balances two competitive objective functions, which are defined to improve the prediction utilized variables and reduce the error rate simultaneously. To generate Pareto optimal solutions, an Elastic net approach is employed to eliminate unrelated and redundant variables to increase the accuracy. Afterward, we propose a novel method for combining solutions and ensuring that a subset of features is optimal. Two various real datasets evaluate the proposed price prediction method. The results support the suggested superiority of the model concerning its relative root mean square error and adjusted correlation coefficient.