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Case-based classification system with clustering for automotive engine spark ignition diagnosis
Vong C.M.; Wong P.K.; Ip W.F.
2010-11-29
Conference Name2010 IEEE/ACIS 9th International Conference on Computer and Information Science
Source PublicationProceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010
Pages17-22
Conference Date18-20 Aug. 2010
Conference PlaceYamagata, Japan
Abstract

Most of the pattern classification systems employ AI techniques. The most popular one is multi-layer perceptron network (MLP) because of its high computational efficiency. However, there may be some drawbacks: long training time, adjustment of hyperparameters, only a single most probable classification can be returned, etc. In this paper, case-based reasoning (CBR) approach is presented to help solve these drawbacks. One of the advantages of CBR is that multiple possible classifications for a new case can be provided to the user, who can interactively finalize the correct classification. CBR is effective, however inefficient in time because every instance in a case base must be compared during reasoning. To overcome this inefficiency, a clustering technique of kernel K-means (KKM) is employed. To illustrate the effectiveness and efficiency of CBR and clustering framework, an automotive engineering diagnostic problem is shown. Its result is also compared to that of MLP. Experimental results show that CBR even outperforms than MLP. © 2010 IEEE.

KeywordAutomotive Engine Spark Ignition Signal Diagnosis Case Based Reasoning Clustering Expert System
DOI10.1109/ICIS.2010.18
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Vong C.M.,Wong P.K.,Ip W.F.. Case-based classification system with clustering for automotive engine spark ignition diagnosis[C],2010:17-22.
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