Summary of A Novel Decision Fusion Approach For Sale Price Prediction Using Elastic Net and Mopso, by Amir Eshaghi Chaleshtori
A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO
by Amir Eshaghi Chaleshtori
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel decision level fusion approach to select informative variables in price prediction, which is crucial for overcoming the challenge posed by the increasing number of correlated variables that affect the dependent variable. By introducing a metaheuristic algorithm that balances two objective functions, this study aims to improve the prediction accuracy and reduce the error rate simultaneously. The Elastic net approach is employed to eliminate unrelated and redundant variables, allowing for more accurate predictions. A novel method for combining solutions is also proposed to ensure the optimality of feature subsets. The proposed method is evaluated using two real-world datasets, demonstrating its superiority in terms of relative root mean square error and adjusted correlation coefficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions about prices by choosing the most important factors that affect them. It’s like trying to guess what someone will pay for something based on how much they usually spend on similar things. The researchers came up with a new way to combine different clues, or variables, to get a more accurate answer. They tested their method using real data from two different sources and found it worked really well. |
