Name
Abstract | ||
|---|---|---|
Statistical classification methods based on consensus from several data sources are considered. The methods need weighting mechanisms to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and non-linear methods are considered for the optimization. A non-linear method which utilizes a neural network is proposed and gives excellent results in experiments. Consensus theory optimized with neural networks outperforms all other methods both in terms of training and test accuracies in the experiments. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/ICASSP.1996.550780 | Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference |
Keywords | Field | DocType |
neural nets,optimisation,pattern classification,statistical analysis,data sources,linear method,neural network,nonlinear method,optimized consensus theory,statistical classification methods,test accuracies,training,weighting mechanisms | Data source,Data mining,Weighting,Pattern recognition,Computer science,Probability distribution,Decision theory,Artificial intelligence,Consensus theory,Statistical classification,Artificial neural network,Bayesian probability | Conference |
Volume | ISSN | ISBN |
6 | 1520-6149 | 0-7803-3192-3 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. A. Benediktsson | 1 | 860 | 83.81 |
| Johannes R. Sveinsson | 2 | 1150 | 95.58 |
| P. H. Swain | 3 | 327 | 77.70 |