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Hierarchical classification scheme based on identification, isolation and analysis of conflictive regions
Cariño Corrales, Jesús Adolfo; Zurita Millán, Daniel; Delgado Prieto, Miguel; Ortega Redondo, Juan Antonio; Romero Troncoso, Rene De Jesus
Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica; Universitat Politècnica de Catalunya. MCIA - Centre MCIA Innovation Electronics
Abstract: A great deal of effort is being made to increase accuracy and reliability of Condition Based Maintenance systems; for instance, by improved feature selection strategies or optimization approaches of classifier parameters. In this work a novel classification methodology is presented, covering from the characterization of the acquired physical magnitudes to the configuration of the classification algorithms. The proposed methodology provides a more accurate classification structure by identifying and isolating conflictive regions in the classification space and by specialized feature reduction and classification stages for them. The proposed Hierarchical Classification Scheme is composed by sequential layers, in which the clear membership regions are identified first, and the conflictive regions of classification are tackled in upper levels. Such treatment of the conflictive regions is based on new feature space transformation to provide an optimized data understanding and, then, better chances of classification. Improving classification with this method compared to other alternatives implies the avoidance of over-fitting the classification training. Also, the proposed methodology, due to its hierarchical structure nature, offers the opportunity to configure the feature reduction and classification algorithms to obtain the optimal data management.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Enginyeria electrònica
Artificial intelligence
keywords: {condition monitoring
feature selection
maintenance engineering
mechanical engineering computing
pattern classification
support vector machines
classification algorithm configuration
classification training
condition-based maintenance systems
conflictive region analysis
conflictive region identification
conflictive region isolation
feature reduction
feature space transformation
hierarchical classification scheme
membership regions
optimal data management
physical magnitudes
sequential layers
upper levels
Accuracy
Databases
Degradation
Feature extraction
Kernel
Support vector machines
Training
Artificial Intelligence
Classification Algorithms
Condition Monitoring
Feature extraction
Machine Learning
Support Vector Machines}
Intel·ligència artificial
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Institute of Electrical and Electronics Engineers (IEEE)
         

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