A Latent Factor-Based Bayesian Neural Networks Model in Cloud Platform for Used Car Price Prediction
Document Type
Article
Publication Date
1-1-2024
Abstract
The selling price of a used car can be predicted based on its historical information. Accurate and reasonable used car price evaluation will be able to promote the healthy progress of the used car industry. Current used car price prediction models are troubled by data quality and the inability to provide estimates of uncertain information, and the prediction accuracy cannot meet the needs of real-world scenarios. In this work, we propose a price prediction model based on a Bayesian neural networks with latent factor (LFBNN) in a cloud platform, which is capable of performing latent factor extraction operations on structured data of used cars as a way to remove the effect of noise in the dataset on the model performance. Moreover, the weight parameters of the Bayesian neural networks (NNs) are represented as probability distributions, which is equivalent to introducing uncertainty and acting as a regularization effect compared to a NN with fixed weights, thus making it possible to alleviate the overfitting problem. The LFBNN model uses a cloud platform for data transmission and storage, enabling it to run faster. Compared with the current benchmark models, the model proposed in this work achieves excellent experimental results on two real datasets. The experimental results on the $Car_{1}$ dataset are 1.498 for mse, 1.224 for rmse, and 0.995 for MAE, and 1.519 for mse, 1.232 for rmse, and 1.002 for MAE on the $Car_{2}$ dataset.
Keywords
Automobiles, Predictive models, Bayes methods, Artificial neural networks, Data models, Data mining, Mathematical models, Bayesian neural networks (NNs), latent factor, price prediction model, used car
Divisions
fsktm,Faculty_of_Business_and_Accountancy
Publication Title
IEEE Transactions on Engineering Management
Volume
71
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA