On-line Valuation of Residential Premises with Evolving Fuzzy Models
Sprache des Titels:
Proceedings of the Hybrid Artificial Intelligence Systems Conference, HAIS 2011
In this paper, we investigate on-line fuzzy modeling for predicting
the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (achieved through fuzzy sets and fuzzy rule bases). We use the FLEXFIS approach as learning engine for evolving fuzzy (regression) models, exploiting the Takagi-Sugeno fuzzy model architecture. The comparison with state-of-the-art expert-based premise estimation was based on a real-world data set including prices for residential premises within the years 1998 to 2008, and showed that FLEXFIS was able to out-perform expert-based method.