Name
Abstract | ||
|---|---|---|
An outstanding problem is how to make decisions with uncertain and incomplete data from disparate sources without NP-hard algorithms. Here we introduce a new reasoning methodology, fuzzy-inferenced decisionmaking (FIND), to solve this problem in polynomial time. In this methodology, a fuzzy-belief-state base (FBSB) is created from historical data of the states of a system by clustering the set of values for each state variable into three clusters upon whose center fuzzy set membership functions LOW, MEDIUM and HIGH are defined. The FBSB is mined for fuzzy association rules using the fuzzy set memberships to infer values for the missing data via these rules. When given an incomplete and uncertain observation of the system state, the observed state is completed via fuzzy association rules. Then each case in the FBSB is matched against the inference-completed observation to retrieve the best matching fuzzy belief state record that contains a decision as an extra variable. The process is analogous to case-based reasoning, but it uses fuzzification to ameliorate uncertainty and to complete missing data. The test results on real world data prove the effectiveness of this methodology. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.asoc.2011.01.026 | Appl. Soft Comput. |
Keywords | Field | DocType |
incompleteness,fuzzy-inferenced decisionmaking,fsmf,fbsbr,fbsb,fuzzy belief state record,fuzzy-belief-state-based reasoning,fuzzy-belief-state,fuzzy-belief-state base,find,decisionmaking,fuzzy set membership function,center fuzzy set membership,fuzzy set membership,fuzzy-belief state,association rule mining,real world data,fbs,fuzzy association rules,observed state,uncertainty,historical data,state variable,incomplete data,fuzzy association rule,missing data,polynomial time,membership function,case base reasoning,fuzzy set | Data mining,Fuzzy classification,Fuzzy logic,Fuzzy set,Association rule learning,Artificial intelligence,Missing data,Fuzzy number,Type-2 fuzzy sets and systems,Cluster analysis,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 4 | Applied Soft Computing Journal |
Citations | PageRank | References |
2 | 0.40 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lily R. Liang | 1 | 143 | 11.40 |
| Carl G. Looney | 2 | 198 | 21.58 |
| Vinay Mandal | 3 | 86 | 2.67 |