文章基本信息

标题：Applying Unascertained Theory, Principal Component Analysis and ACO-based Artificial Neural Networks for Real Estate Price Determination
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作者：Li, Wanqing ; Shi, Huawang
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2011
卷号：6
期号：9
页码：1672-1679
DOI：10.4304/jsw.6.9.1672-1679
语种：English
出版社：Academy Publisher
摘要：Real estate industry is both capital-intensive, highly related industries and industries essential to provide the daily necessities. However, the real estate pricing models and methods of research rarely receive the critical attention and development it deserves. In this paper, we present a multi-resolution approach for the determination of the real estate pricing. The proposed method firstly utilizes unascertained theory to describe and quantity the price indices of the real estate, then principal component analysis (PCA) were introduced in to eliminate the real estate pricing indices having the relativities and overlap information. The representative indices from principal component analysis process substitute for the primary indexes. Thus subjective random problem in choosing indices can be avoided. Finally, Using ACO-based artificial neural networks, real estate pricing was analyzed and the results show that this method is more convenient and practical compared with the traditional one.
关键词：unascertained theory;principal component analysis;ant colony optimization;artificial neural networks;real estate;price determination