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1 Fuzzy Signatures in SARS Student: Bai Qifeng Client: Prof. Tom Gedeon.

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Presentation on theme: "1 Fuzzy Signatures in SARS Student: Bai Qifeng Client: Prof. Tom Gedeon."— Presentation transcript:

1 1 Fuzzy Signatures in SARS Student: Bai Qifeng Client: Prof. Tom Gedeon

2 2 Problem of Fuzzy Theory A Major issue in fuzzy applications is how to create fuzzy rules the number of rules have an exponential increase with the number of inputs and terms. At least one activated rule for every input. e.g. 5 terms, 2 inputs => 25 rules 5 terms, 5 inputs => 3,125 rules

3 3 Fuzzy Signature Fuzzy signatures structure data into vectors of fuzzy values, each of which can be a further vector. Fuzzy signatures can be regarded as Special multidimensional fuzzy data. Some of the dimensions are interrelated in the sense that they form sub-group of variables, which jointly determine some feature on a higher level.

4 4 Fuzzy Signature

5 5 Fuzzy Signatures The relationship between higher and lower levels is govern by fuzzy aggregations. Appropriate aggregations used in their child signatures are not necessary identical. They can be changed based on expert opinions and detailed circumstance.

6 6 Fuzzy Signatures in SARS The following scheme is of some daily symptom signatures of patients:

7 7 Automatically Construct Fuzzy Signature Searches the features of data structure, classify data based on their relevance and cluster the high relevant data. With clustering, the known situation can be used to build the model and then it can be used to another situation where it is not known.

8 8 Fuzzy Clustering Hierarchical Method Creating a cluster tree. Objective Function Method Solving problems about fuzzy boundary in evaluating relevance of objects

9 9 Data Pretreatment Standardizing raw data Outlier Two-stage method Scatter Plot Missing data

10 10 Conclusion Choosing proper cluster algorithm and appropriate data pretreatment, try to find the appropriate fuzzy signatures After constructing the fuzzy signature, with aggregations, we can effectively reduce the number of rules in this fuzzy system.


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