A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

作     者：Ping-Huan Kuo Sing-Yan Chen Her-Terng Yau 

作者机构：Department of Mechanical EngineeringNational Chung Cheng UniversityChiayi62102TaiwanChina Advanced Institute of Manufacturing with High-Tech Innovations(AIM-HI)National Chung Cheng UniversityChiayi62102TaiwanChina 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：1577-1596页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Ministry of Science and Technology Taiwan under Grants MOST 111-2218-E-194-007 

主　　题：Used car price prediction transfer learning fuzzy logic machine learning optimization algorithm 

摘      要：Cars are regarded as an indispensable means of transportation in *** studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly *** this study,a price prediction system for used BMW cars was *** parameters of used cars,including their model,registration year,and transmission style,were *** data obtained were then divided into three *** first subset was used to compare the results of each *** predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural *** second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model ***,the third subset was used for the validation set during the prediction *** three subsets were divided using k-fold cross-validation to avoid overfitting and selection *** conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully *** prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.