On employing fuzzy modeling algorithms for the valuation of residential premises
Edwin Lughofer , Bogdan Trawiński , Krzysztof Trawiński , Olgierd Kempa , Tadeusz Lasota
AbstractIn this paper, we investigate fuzzy modeling techniques for predicting the prices of residential premises, based on some main drivers such as usable area of premises, age of a building, number of rooms in a flat, floor on which a flat is located, number of storeys in a building as well as the distance from the city center. Our proposed modeling techniques rely on two aspects: the first one (called SparseFIS) is a batch off-line modeling method and tries to out-sparse an initial dense rule population by optimizing the rule weights within an iterative optimization procedure subject to constrain the number of important rules; the second one (called FLEXFIS) is a single-pass incremental method which is able to build up fuzzy models in an on-line sample-wise learning context. As such, it is able to adapt former generated prediction models with new data recordings on demand and also to cope with on-line data streams. The final obtained fuzzy models provide some interpretable insight into the relations between the various features and residential prices in form of linguistically readable rules (IF-THEN conditions). Both methods will be compared with a state-of-the-art premise estimation method usually conducted by many experts and exploiting heuristic concepts such as sliding time window, nearest neighbors and averaging. The comparison is based on a two real-world data set including prices for residential premises within the years 1998-2008.
|Journal series||Information Sciences, ISSN 0020-0255, e-ISSN 1872-6291, (A 40 pkt)|
|Publication size in sheets||0.95|
|Keywords in English||FLEXFIS; Fuzzy modeling; Incremental on-line learning; Residential premise prices; Rule weights; SparseFIS|
|ASJC Classification||; ; ; ; ;|
|Publication indicators||= 41; = 38; : 2011 = 2.815; : 2011 = 2.833 (2) - 2011=2.984 (5)|
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