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.

翻译：价格预测算法根据市场趋势、预期需求及其他特征（包括政府法规、国际交易、投机预期等因素）为每种商品或服务设定价格。作为价格预测中的因变量，其受多个独立及相关变量影响，这为价格预测带来挑战。为应对这一挑战，机器学习算法无需显式建模变量间的关联性即可实现更精准的价格预测。然而，随着输入变量增加，现有机器学习方法在计算效率和预测有效性方面面临挑战。为此，本研究提出一种新型决策级融合方法，用于筛选价格预测中的信息性变量。该元启发式算法通过平衡两个竞争性目标函数——同时提升预测变量利用效率并降低误差率。为生成帕累托最优解，采用弹性网络方法剔除无关及冗余变量以提高精度。进而提出一种创新的解融合方法，确保特征子集达到最优。基于两个不同真实数据集对所提价格预测方法进行评估，结果表明该模型在相对均方根误差和调整后的相关系数方面具有显著优越性。