Name
Abstract | ||
|---|---|---|
The Choquet integral is a very useful tool for multiple resource information fusion. Also, the case-based reasoning (CBR) can serve as the information fusion tool based on the basic idea “similar problems have similar solutions”. But the similarity measure among diverse cases has been studied with little satisfaction in the past decades. In this paper we take arbitrary number of similar case distances as the input of the Choquet integral to flexibly represent the interaction among the cases. Consequently, our proposed approach has the ability to approximate the more general relation described by a CBR system. Because of the application of the Choquet integral and the fact that the existing CBR system can be regarded as a special case of our proposed approach, we largely generalize the application scope of traditional CBR techniques. Essentially, our proposed approach can work well based on incomplete data and also tolerate noisy data and outliers. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLC.2010.5581073 | ICMLC |
Keywords | Field | DocType |
incomplete data,multiple resource information fusion,case-based reasoning,fuzzy measure,clustering,choquet integral,clustering algorithms,parameter estimation,case based reasoning,case base reasoning,cybernetics,machine learning | Similarity measure,Computer science,Outlier,Artificial intelligence,Estimation theory,Choquet integral,Case-based reasoning,Cluster analysis,Machine learning,Cybernetics,Special case | Conference |
Volume | ISBN | Citations |
1 | 978-1-4244-6526-2 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shihong Yue | 1 | 81 | 17.58 |
| Weiqing Li | 2 | 15 | 5.77 |
| Jing Zhao | 3 | 79 | 14.50 |
| Xian Zhao | 4 | 0 | 1.69 |