Dynamic Decision Method Based on Contextual Selection of Representation Subspaces

Pierre Beauseroy (1) , André Smolarz (1) , Yuan Dong (1) , Xiyan He (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored system property. The main idea is to divide the decision into several partial decisions and then to aggregate these to get the final one. The adaptation is the result of the aggregation process which aims at selecting and summarizing the partial decisions which are based on coherent information according to learnt models. The suggested method is presented. Experiments on a two-class image segmentation problem are performed and analyzed. The results assessed that the suggested method is more robust when an abrupt change occurs and is able to select efficiently the partial decision makers. This approach opens a wide field of applications and results are very encouraging.

Domains: Machine Learning [cs.LG]

Pierre Beauseroy, André Smolarz, Yuan Dong, Xiyan He. Dynamic Decision Method Based on Contextual Selection of Representation Subspaces. 2010 International Conference on Machine Learning and Applications (ICMLA), Dec 2010, Washington, United States. pp.567-572, ⟨10.1109/ICMLA.2010.88⟩. ⟨hal-02518619⟩