@inBook{
 title = {A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Problem},
 type = {inBook},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {comprehensive evaluation,feature selection,fuzzy correlation projection,target recognition},
 pages = {3-11},
 volume = {7390},
 websites = {http://dl.acm.org/citation.cfm?id=2364191.2364279,http://link.springer.com/10.1007/978-3-319-42291-6_1},
 publisher = {Springer International Publishing},
 editors = {[object Object],[object Object],[object Object]},
 id = {dd3420b9-007a-36b5-b1c6-5bb83b1c442b},
 created = {2016-08-24T12:36:40.000Z},
 file_attached = {true},
 profile_id = {ff99ac45-7683-3f97-9d67-cdc65d8478df},
 last_modified = {2017-03-22T02:31:08.099Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 abstract = {Individual optimal feature selection algorithm is simple and effective. Generally, single criterion is chosen for feature selection. But in this case, it is possible that features with good performance on other criteria will be neglected, and causing a negative impact to feature selection result. For the problem, the paper proposes a comprehensive evaluation model based on fuzzy correlation projection for evaluating the comprehensive indexs of individual features, and takes the results as the basis of feature selection. Definition to the model and its application procedure are described. Finally, the proposed method is applied to evaluate and select the underwater target recognition feature. The experimental result shows that the selected feature subset based on comprehensive indexs has a higher testing recognition rate than that based on single criterion, e.g. Relief-F indexs. Therefore, the proposed individual feature evaluation and selection method based on comprehensive indexs is feasible and effective.},
 bibtype = {inBook},
 author = {Franco, Carlos and López-Santana, Eduyn and Mendez-Giraldo, Germán},
 book = {Intelligent Computing Theories and Application}
}

{"_id":"QMywmQRaZPXigZ5ka","bibbaseid":"franco-lpezsantana-mendezgiraldo-avariableneighborhoodsearchapproachforthecapacitatedmringstarproblem-2016","downloads":0,"creationDate":"2017-06-27T22:32:19.868Z","title":"A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Problem","author_short":["Franco, C.","López-Santana, E.","Mendez-Giraldo, G."],"year":2016,"bibtype":"inBook","biburl":null,"bibdata":{"title":"A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Problem","type":"inBook","year":"2016","identifiers":"[object Object]","keywords":"comprehensive evaluation,feature selection,fuzzy correlation projection,target recognition","pages":"3-11","volume":"7390","websites":"http://dl.acm.org/citation.cfm?id=2364191.2364279,http://link.springer.com/10.1007/978-3-319-42291-6_1","publisher":"Springer International Publishing","editors":"[object Object],[object Object],[object Object]","id":"dd3420b9-007a-36b5-b1c6-5bb83b1c442b","created":"2016-08-24T12:36:40.000Z","file_attached":"true","profile_id":"ff99ac45-7683-3f97-9d67-cdc65d8478df","last_modified":"2017-03-22T02:31:08.099Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"abstract":"Individual optimal feature selection algorithm is simple and effective. Generally, single criterion is chosen for feature selection. But in this case, it is possible that features with good performance on other criteria will be neglected, and causing a negative impact to feature selection result. For the problem, the paper proposes a comprehensive evaluation model based on fuzzy correlation projection for evaluating the comprehensive indexs of individual features, and takes the results as the basis of feature selection. Definition to the model and its application procedure are described. Finally, the proposed method is applied to evaluate and select the underwater target recognition feature. The experimental result shows that the selected feature subset based on comprehensive indexs has a higher testing recognition rate than that based on single criterion, e.g. Relief-F indexs. Therefore, the proposed individual feature evaluation and selection method based on comprehensive indexs is feasible and effective.","bibtype":"inBook","author":"Franco, Carlos and López-Santana, Eduyn and Mendez-Giraldo, Germán","book":"Intelligent Computing Theories and Application","bibtex":"@inBook{\n title = {A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Problem},\n type = {inBook},\n year = {2016},\n identifiers = {[object Object]},\n keywords = {comprehensive evaluation,feature selection,fuzzy correlation projection,target recognition},\n pages = {3-11},\n volume = {7390},\n websites = {http://dl.acm.org/citation.cfm?id=2364191.2364279,http://link.springer.com/10.1007/978-3-319-42291-6_1},\n publisher = {Springer International Publishing},\n editors = {[object Object],[object Object],[object Object]},\n id = {dd3420b9-007a-36b5-b1c6-5bb83b1c442b},\n created = {2016-08-24T12:36:40.000Z},\n file_attached = {true},\n profile_id = {ff99ac45-7683-3f97-9d67-cdc65d8478df},\n last_modified = {2017-03-22T02:31:08.099Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n abstract = {Individual optimal feature selection algorithm is simple and effective. Generally, single criterion is chosen for feature selection. But in this case, it is possible that features with good performance on other criteria will be neglected, and causing a negative impact to feature selection result. For the problem, the paper proposes a comprehensive evaluation model based on fuzzy correlation projection for evaluating the comprehensive indexs of individual features, and takes the results as the basis of feature selection. Definition to the model and its application procedure are described. Finally, the proposed method is applied to evaluate and select the underwater target recognition feature. The experimental result shows that the selected feature subset based on comprehensive indexs has a higher testing recognition rate than that based on single criterion, e.g. Relief-F indexs. Therefore, the proposed individual feature evaluation and selection method based on comprehensive indexs is feasible and effective.},\n bibtype = {inBook},\n author = {Franco, Carlos and López-Santana, Eduyn and Mendez-Giraldo, Germán},\n book = {Intelligent Computing Theories and Application}\n}","author_short":["Franco, C.","López-Santana, E.","Mendez-Giraldo, G."],"urls":{"Paper":"http://bibbase.org/service/mendeley/ff99ac45-7683-3f97-9d67-cdc65d8478df/file/4753d978-3ec9-8ca3-4837-e8652af0b65f/2016-A_Variable_Neighborhood_Search_Approach_for_the_Capacitated_m-Ring-Star_Problem.pdf.pdf","Website":"http://dl.acm.org/citation.cfm?id=2364191.2364279,http://link.springer.com/10.1007/978-3-319-42291-6_1"},"bibbaseid":"franco-lpezsantana-mendezgiraldo-avariableneighborhoodsearchapproachforthecapacitatedmringstarproblem-2016","role":"author","keyword":["comprehensive evaluation","feature selection","fuzzy correlation projection","target recognition"],"downloads":0},"search_terms":["variable","neighborhood","search","approach","capacitated","ring","star","problem","franco","lópez-santana","mendez-giraldo"],"keywords":["comprehensive evaluation","feature selection","fuzzy correlation projection","target recognition"],"authorIDs":[]}