Probabilistic Decision Trees using SVM for Multi-class Classification
TypeCommunications avec actes
In the automotive repairing backdrop, retrieving from previously solved incident the database features that could support and speed up the diagnostic is of great usefulness. This decision helping process should give a fixed number of the more relevant diagnostic classified in a likelihood sense. It is a probabilistic multi-class classification problem. This paper describes an original classification technique, the Probabilistic Decision Tree (PDT) producing a posteriori probabilities in a multi-class context. It is based on a Binary Decision Tree (BDT) with Probabilistic Support Vector Machine classifier (PSVM). At each node of the tree, a bi-class SVM along with a sigmoid function are trained to give a probabilistic classification output. For each branch, the outputs of all the nodes composing the branch are combined to lead to a complete evaluation of the probability when reaching the final leaf (representing the class associated to the branch). To illustrate the effectiveness of PDTs, they are tested on benchmark datasets and results are compared with other existing approaches.
Showing items related by title, author, creator and subject.
MECHBAL, Nazih; URIBE, Juan Sebastian; REBILLAT, Marc (Elsevier, 2015)In this paper, a probabilistic multi-class pattern recognition algorithm is developed for damage detection, localization, and quantification in smart mechanical structures. As these structures can face damages of different ...
Simultaneous Influence of Static Load and Temperature on the Electromechanical Signature of Piezoelectric Elements Bonded to Composite Aeronautic Structures REBILLAT, Marc; GUSKOV, Mikhail; BALMES, Etienne; MECHBAL, Nazih (ASME, 2016)Electromechanical (EM) signature techniques have raised a huge interest in the structural health-monitoring community. These methods aim at assessing structural damages and sensors degradation by analyzing the EM responses ...
Peaks Over Threshold–based detector design for structural health monitoring: Application to aerospace structures REBILLAT, Marc; HMAD, Ouadie; KADRI, Farid; MECHBAL, Nazih (SAGE Journals, 2018)Structural health monitoring offers new approaches to interrogate the integrity of complex structures. The structural health monitoring process classically relies on four sequential steps: damage detection, localization, ...
Automatic Damage Quantification Using Signal Based And Nonlinear Model Based Damage Sensitive Features GHRIB, Meriem; REBILLAT, Marc; MECHBAL, Nazih; VERMOT DES ROCHES, Guillaume (2017)Structural Health Monitoring (SHM) can be de ned as the process of acquiring and analyzing data from on-board sensors to evaluate the health of a structure. Classically, an SHM process can be performed in four steps: ...