Housing price forecast based on rough-set extreme learning machine

Benxue Wang

Institute for Economic and Social Development, Quzhou University, Zhejiang, 324000, China

The work, based on various factors affecting housing price in 31 provinces cities as research object, firstly adopted rough set theory to reduce those factors. Then, the main reduced influence factors were used as the input of extreme learning machine. On such basis, the housing price forecast model based on rough-set extreme learning machine was ultimately established. According to the simulation results, the algorithm in this work has good prediction effect, and its prediction precision is higher than that of BP neural network and RBF neural network. Therefore, this algorithm, with a certain practical and theoretical value, can be promoted to other areas for predication and classification.