Name
Abstract | ||
|---|---|---|
Since industrial processes have a wide range of operating conditions, it is difficult to build a single global model that describes a process. One solution that is widely used in control engineering practice is to combine multiple models based on collected process data. For this approach to be successful, it is important to cluster the data before the modeling. In this study, pairwise constraints and an active-learning method were incorporated into the affinity propagation algorithm, resulting in a new method called active semi-supervised affinity propagation (ASSAP) clustering. To apply ASSAP to the modeling of industrial processes, an active-learning strategy is firstly used to obtain constraints on data based on the angle of change between two data points and the probability of their belonging to the same class, and then the constraints are used to adjust the clustering process so as to improve the clustering precision. Finally, the least-squares-support-vector-machine (LS-SVM) is used to build a submodel for each cluster of data points, and then all the sub-models are integrated into a model for the whole data set. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.engappai.2016.08.002 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Active learning,Affinity propagation (AP),Complex industrial process,Least-squares support vector machine (LS-SVM),Multimodel approach,Pairwise constraints,Semi-supervised clustering | Data point,Pairwise comparison,Data mining,Data stream clustering,Active learning,Affinity propagation,Correlation clustering,Computer science,Artificial intelligence,Cluster analysis,Machine learning,Multiple Models | Journal |
Volume | ISSN | Citations |
56 | 0952-1976 | 1 |
PageRank | References | Authors |
0.38 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei, Q. | 1 | 6 | 3.26 |
| Huiping Yu | 2 | 1 | 0.38 |
| Min Wu | 3 | 3582 | 272.55 |
| Jin-Hua She | 4 | 1841 | 182.27 |