| 引用本文: | 秦心静, 章 平, 张新杨.基于位置子市场划分的房价贝叶斯概率模型(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（5）：81-88 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 针对方案属性值为Vague值且考虑专家评分可信度的多属性群决策问题,提出了一种基于Vague集模糊熵和D-S证据理论的多属性群决策分析方法。该方法充分考虑各专家给出的Vague值评价信息中所蕴含的模糊性与不确定性,借助模糊熵来获取与专家自身意见相匹配的评分可信度序列,其完全由数据驱动,弥补了传统方法对可信度主观统一设定的不足。首先,基于各专家原始决策矩阵获得各属性下的Vague集模糊熵,以构建与专家集相对应的评分可信度矩阵;其次,对经可信度调整后的各专家决策矩阵使用证据合成进行信息集结,利用Vague集记分函数并经可信度调整得到属性权重;最后,将专家群体集结信息经属性权重加权修正后算出各方案最终的Vague评价值,进而使用记分函数获得各方案综合得分,筛选出最优方案。利用证据理论在不确定信息融合方面的优势和Vague集记分函数的信息转化功能,通过证据合成和记分函数集结专家群体的评价信息,所得出的决策结果更加客观、合理,并通过一个具体算例验证了所提方法的可行性和有效性。 |
| 关键词: 房价评估 HPM 模型 概率模型 位置特征 子市场 |
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| Bayesian Probability Model for Real Estate Price Based on Location Submarket Segmentation |
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QIN Xinjing, ZHANG Ping, ZHANG Xinyang
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School of Computer and Information, Anhui Polytechnic University, Anhui Wuhu 241000, China
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| Abstract: |
| A Bayesian probability model based on sub-market effects was proposed to address the situation that the hedonic
price model HPM is prone to insufficient prediction accuracy and interpretability in the face of the complex relationship
between house prices and characteristics. In improving the algorithm design the idea of sub-market clustering was
borrowed a latent variable was introduced to represent sub-markets and sub-market criteria were established based on
location proximity and substitutability. Next sub-market criteria and hedonic price models were used as probabilistic
dependencies of Bayesian networks to determine the range of effects in each sub-market completing the sub-market
segmentation. Finally house prices were predicted based on the probabilities of the submarkets to which the houses
belonged and the key influencing factors of the submarkets were analyzed to improve the prediction accuracy and
interpretability. The model was compared with five existing models in terms of mean absolute percentage error mean
absolute error and root mean square error. The performance of the algorithms of the non-submarket model and the
submarket model were tested separately based on property data of Hangzhou City before 2019. The experiments show that
the Bayesian model outperforms the comparison models in terms of accuracy in forecasting real estate prices and has the
advantage of interpretability. |
| Key words: house price assessment Hedonic Price Model (HPM) probability model location characteristics submarket |
