@ARTICLE{Kimotho2014b,
  author = {Kimotho, James Kuria and Sextro, Walter},
  title = {Optimal Parameter Tuning for Multiclass Support Vector Machines in
	Machinery Health State Estimation},
  journal = {PAMM},
  year = {2014},
  volume = {14},
  pages = {815--816},
  number = {1},
  abstract = {The increasing demand for high reliability, safety and availability
	of technical systems calls for innovative maintenance strategies.
	The use of prognostic health management (PHM) approach where maintenance
	action is taken based on current and future health state of a component
	or system is rapidly gaining popularity in the maintenance industry.
	Multiclass support vector machines (MC-SVM) has been identified as
	a promising algorithm in PHM applications due to its high classification
	accuracy. However, it requires parameter tuning for each application,
	with the objective of minimizing the classification error. This is
	a single objective optimization problem which requires the use of
	optimization algorithms that are capable of exhaustively searching
	for the global optimum parameters. This work proposes the use of
	hybrid differential evolution (DE) and particle swarm optimization
	(PSO) in optimally tuning the MC-SVM parameters. DE identifies the
	search limit of the parameters while PSO finds the global optimum
	within the search limit. The feasibility of the approach is verified
	using bearing run-to-failure data and the results show that the proposed
	method significantly increases health state classification accuracy.},
  doi = {10.1002/pamm.201410388},
  file = {Kimotho2014b.pdf:Kimotho2014b.pdf:PDF},
  issn = {1617-7061},
  owner = {tobiasm},
  publisher = {WILEY-VCH Verlag},
  timestamp = {2016.01.15}
}