GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis
Traditional decision trees for fault diagnosis often use an ID3 construction algorithm. For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.140637-140650 |
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| creator | Shi, Junyou He, Qingjie Wang, Zili |
| description | Traditional decision trees for fault diagnosis often use an ID3 construction algorithm. For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The first highlight is to propose a CV-DT which is constructed by an improved ID3 algorithm considering the cluster validity index. A new cluster validity index which can compare the cluster validities of different attributes is proposed to modify the information gain. This method selects the splitting attributes with higher classification credibility and increases the diagnostic accuracy. The second highlight is to propose an FR-DT which is constructed by an improved ID3 algorithm considering the fault rates. This algorithm not only considers the partitioning ability of each attribute, but also considers the isolation priority of faults with higher fault rates. This method decreases the average diagnostic steps and promotes the diagnostic efficiency. Through a simulation case and a real board case, these decision trees are proved to be effective diagnostic tools which have higher accuracies or efficiencies in analog circuit. |
| doi_str_mv | 10.1109/ACCESS.2019.2943380 |
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For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The first highlight is to propose a CV-DT which is constructed by an improved ID3 algorithm considering the cluster validity index. A new cluster validity index which can compare the cluster validities of different attributes is proposed to modify the information gain. This method selects the splitting attributes with higher classification credibility and increases the diagnostic accuracy. The second highlight is to propose an FR-DT which is constructed by an improved ID3 algorithm considering the fault rates. This algorithm not only considers the partitioning ability of each attribute, but also considers the isolation priority of faults with higher fault rates. This method decreases the average diagnostic steps and promotes the diagnostic efficiency. 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(IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-9dab042b9cd4b9fb2b54e9044bb8e6582d094d96f4167679349a924b33e6ed0b3</citedby><cites>FETCH-LOGICAL-c408t-9dab042b9cd4b9fb2b54e9044bb8e6582d094d96f4167679349a924b33e6ed0b3</cites><orcidid>0000-0003-4008-6622</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8847300$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,4025,27635,27925,27926,27927,54935</link.rule.ids></links><search><creatorcontrib>Shi, Junyou</creatorcontrib><creatorcontrib>He, Qingjie</creatorcontrib><creatorcontrib>Wang, Zili</creatorcontrib><title>GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis</title><title>IEEE access</title><addtitle>Access</addtitle><description>Traditional decision trees for fault diagnosis often use an ID3 construction algorithm. For promoting the accuracy and efficiency of decision trees, considering the cluster validity and fault rates, this paper proposes two improved trees, CV-DTs and FR-DTs. This paper mainly has two highlights. The first highlight is to propose a CV-DT which is constructed by an improved ID3 algorithm considering the cluster validity index. A new cluster validity index which can compare the cluster validities of different attributes is proposed to modify the information gain. This method selects the splitting attributes with higher classification credibility and increases the diagnostic accuracy. The second highlight is to propose an FR-DT which is constructed by an improved ID3 algorithm considering the fault rates. This algorithm not only considers the partitioning ability of each attribute, but also considers the isolation priority of faults with higher fault rates. This method decreases the average diagnostic steps and promotes the diagnostic efficiency. Through a simulation case and a real board case, these decision trees are proved to be effective diagnostic tools which have higher accuracies or efficiencies in analog circuit.</description><subject>Algorithms</subject><subject>Analog circuits</subject><subject>Circuit faults</subject><subject>Classification algorithms</subject><subject>Cluster validity</subject><subject>Clustering</subject><subject>Clustering algorithms</subject><subject>decision tree</subject><subject>Decision trees</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>Fault diagnosis</subject><subject>fault rate</subject><subject>GMM clustering</subject><subject>ID3 algorithm</subject><subject>Indexes</subject><subject>Partitioning algorithms</subject><subject>Validity</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkVtL7TAQhYsoKOov8CXgc7e5TNPkcZ96OYIieHsNSTPdZFMbT9I--O9PtVtxXmYY1reGYRXFGaMrxqi-WDfN1dPTilOmV1yDEIruFUecSV2KSsj9X_NhcZrzls6l5lVVHxXp5v6eNP2UR0xh2JR_bEZPLrENOcSBPCfETJo45OC_BOTaTv1IHu2IxA7-GyWvtg8-jB-ki4msB9vHDWlCaqcw7pDLYDdDzCGfFAed7TOe7vpx8XJ99dz8Le8ebm6b9V3ZAlVjqb11FLjTrQenO8ddBagpgHMKZaW4pxq8lh0wWctaC9BWc3BCoERPnTgubhdfH-3WvKfwZtOHiTaYr0VMG2PTGNoeDbcanfJMdtKD4MxWDEBiTWtUHqtu9jpfvN5T_DdhHs02Tml-MxsOVSWBc1nPKrGo2hRzTtj9XGXUfGZllqzMZ1Zml9VMnS1UQMQfQimoBaXiP23-j24</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Shi, Junyou</creator><creator>He, Qingjie</creator><creator>Wang, Zili</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Through a simulation case and a real board case, these decision trees are proved to be effective diagnostic tools which have higher accuracies or efficiencies in analog circuit.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2943380</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4008-6622</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analog circuits Circuit faults Classification algorithms Cluster validity Clustering Clustering algorithms decision tree Decision trees Diagnostic software Diagnostic systems Fault diagnosis fault rate GMM clustering ID3 algorithm Indexes Partitioning algorithms Validity |
| title | GMM Clustering-Based Decision Trees Considering Fault Rate and Cluster Validity for Analog Circuit Fault Diagnosis |
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