A novel decision fusion approach for sale price prediction using Elastic
  Net and MOPSO
Amir Eshaghi Chaleshtori
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.