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.

根据市场趋势、预期需求和其他特征（包括政府规定、国际交易和投机与期望），价格预测算法针对每个产品或服务提出价格。然而，随着输入变量的增加，现有的机器学习方法在计算效率和预测效果方面面临挑战。因此，本研究介绍了一种决策层面融合方法，以选择有信息量的变量进行价格预测，并提出了一个基于元启发式算法的方案，以同时改进预测所使用的变量，并降低误差率。通过使用Elastic net方法消除无关和冗余变量以提高准确性，生成帕累托最优解。随后，我们提出了一种新的方法来合并解决方案，并确保特征子集是最佳的。两个真实数据集评估了所提出的价格预测方法，并且结果支持模型相对均方根误差和调整相关系数的优越性。

使用弹性网络和多目标粒子群优化的销售价格预测的新型决策融合方法