Notice of Violation of IEEE Publication Principles: Research on Urban Land Price Assessment Based on Artificial Neural Network Model

Notice of Violation of IEEE Publication Principles

“Research on Urban Land Price Assessment Based on Artificial Neural Network Model”
by Shousong Cai, Xiaomin Gu, Xiaoguang Wang, Ying Chen
in IEEE Access, Special Section on Urban Computing and Intelligence, December 2019, pp. 180738-180748

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.

This paper copied content from the paper cited below. The original content was translated and copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

“Research on Urban Residential Land Price Assessment Based on Artificial Intelligence Reasoning Model”
by Biao Zhang
in the Masters Thesis, University of Electronic Science and Technology of China, June 2018


At present, there are many problems to be solved in the study of residential land price evaluation, such as the imbalance of residential land price data categories, the small sample of residential land price data sets, the possibility of the evaluation model falling into the local optimal value, and the large error of feature quantification. This paper introduces the problems in the process of residential land price evaluation from three aspects: land price data, evaluation model and feature quantification. In this paper, an extraction method for land price features based on transfer learning is proposed in connection with defects such as the small total amount of residential land price data and unbalanced class distribution, and different land price assessment models are used to carry out a precision evaluation and explore the models and methods suitable for land price assessment. In this paper, the extraction method for residential land price features is improved. The experimental results show that the extraction algorithm for land price features based on transfer learning has better classification accuracy than the commonly used principal component analysis and extraction method and linear normalization method, with a mean average precision 10.9% and 4.73% higher, respectively. The optimal feature set classification accuracy reached 90.28%; therefore, the algorithm for residential land price assessment proposed in this paper is able to satisfy the precision requirement of actual land price assessment.