Name
Abstract | ||
|---|---|---|
In 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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ins.2011.07.012 | Inf. Sci. |
Keywords | Field | DocType |
real-world data,fuzzy model,proposed modeling technique,fuzzy modeling algorithm,single-pass incremental method,new data recording,fuzzy modeling technique,batch off-line modeling method,residential price,on-line data stream,residential premise,prediction model,nearest neighbor | USable,Data mining,Population,Data stream mining,Heuristic,Computer science,Fuzzy logic,Premise,Artificial intelligence,Predictive modelling,Valuation (finance),Machine learning | Journal |
Volume | Issue | ISSN |
181 | 23 | 0020-0255 |
Citations | PageRank | References |
20 | 0.73 | 35 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edwin Lughofer | 1 | 1940 | 99.72 |
| Bogdan Trawiński | 2 | 288 | 24.72 |
| Krzysztof Trawiński | 3 | 247 | 16.06 |
| Olgierd Kempa | 4 | 44 | 2.15 |
| Tadeusz Lasota | 5 | 348 | 25.33 |