Journal
SOFT COMPUTING
Volume 18, Issue 2, Pages 285-297Publisher
SPRINGER
DOI: 10.1007/s00500-013-1057-z
Keywords
Weights of attributes; Importance of attributes; Weighted dimension space; Subspace clustering; Neuro fuzzy system
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The paper presents the neuro-fuzzy system with weighted attributes. Its crucial part is the fuzzy rule base composed of fuzzy rules (implications). In each rule the attributes have their own weights. In our system the weights of the attributes are numbers from the interval [0, 1] and they are not global: each fuzzy rule has its own attributes' weights, thus it exists in its own weighted subspace. The theoretical description is accompanied by results of experiments on real life data sets. They show that the neuro-fuzzy system with weighted attributes can elaborate more precise results than the system that does not apply weights to attributes. Assigning weights to attributes can also discover knowledge about importance of attributes and their relations.
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