A multiple kernel classification approach based on a Quadratic Successive Geometric Segmentation methodology with a fault diagnosis case

作者：Honorio Leonardo M; Barbosa Daniele A; Oliveira Edimar J; Nepomuceno Garcia Paulo A; Santos Murillo F

来源：ISA Transactions, 2018, 74: 209-216.

DOI：10.1016/j.isatra.2018.01.013

• Successive Geometric Segmentation
• Support Vector Machine
• Quadratic classification
• Multiple kernel classifier
• Transformer fault diagnosis

This work presents a new approach for solving classification and learning problems. The Successive Geometric Segmentation technique is applied to encapsulate large datasets by using a series of Oriented Bounding Hyper Box (OBHBs). Each OBHB is obtained through linear separation analysis and each one represents a specific region in a pattern's solution space. Also, each OBHB can be seen as a data abstraction layer and be considered as an individual Kernel. Thus, it is possible by applying a quadratic discriminant function, to assemble a set of nonlinear surfaces separating each desirable pattern. This approach allows working with large datasets using high speed linear analysis tools and yet providing a very accurate non-linear classifier as final result. The methodology was tested using the UCI Machine Learning repository and a Power Transformer Fault Diagnosis real scenario problem. The results were compared with different approaches provided by literature and, finally, the potential and further applications of the methodology were also discussed.

• 出版日期2018-3