Paper Info
Title
Data Resampling Techniques and Specific Algorithms Applied to a Critical Industrial Classification Problem
Abstract
The paper deals with the problem of the detection of rare patterns in an unbalanced dataset related to an industrial problem concerning the identification of manufactured defective metal products on the basis of product and process parameters. Within this work several approaches have been attempted for the development of a classifier whose performance are able to meet the industrial requirements, i.e. a high rate of recognition of defective products. Considered the unbalanced nature of the available dataset, most known techniques used for dealing with this kind of databases (i.e. resampling techniques and specific algorithms) have been investigated and assessed, subsequently the most promising ones have been combined in order to exploit their advantages. This latter combination led to satisfactory results which make the developed classifier usable in the industrial field.
Year
DOI
Venue
2009
10.1109/EMS.2009.30
Athens
Keywords
Field
DocType
industrial field,unbalanced nature,industrial problem,defective product,unbalanced dataset,industrial requirement,high rate,specific algorithms,data resampling techniques,developed classifier usable,manufactured defective metal product,available dataset,critical industrial classification problem,databases,process parameter,decision trees,artificial neural networks,classification,data mining,quality management,data models
USable,Data mining,Data resampling,Decision tree,Data modeling,Computer science,Algorithm,Exploit,Classifier (linguistics),Artificial neural network,Resampling
Conference
ISSN
ISBN
Citations 
2473-3539
978-0-7695-3886-0
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Marco Vannucci19415.60
Valentina Colla215929.50
Gianluca Nastasi3254.61
Nicola Matarese4184.12