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dc.contributor.authorChakraborty, Rudrasisen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorPal, Nikhil R.en_US
dc.date.accessioned2014-12-08T15:36:41Z-
dc.date.available2014-12-08T15:36:41Z-
dc.date.issued2014-09-01en_US
dc.identifier.issn0129-0657en_US
dc.identifier.urihttp://dx.doi.org/10.1142/S012906571450021Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/25042-
dc.description.abstractFor many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks. This scheme sometimes selects redundant groups of features. So, we propose a selection scheme which can control the level of redundancy between the selected groups. The idea is general and can be used with any learning scheme. We have demonstrated the effectiveness of our scheme on several data sets. In this context, we define different measures of sensor dependency (dependency between groups of features). We have also presented an alternative learning scheme which is more effective than our old scheme. The proposed scheme is also adapted to radial basis function (RBS) network. The advantages of our scheme are threefold. It looks at all the groups together and hence can exploit nonlinear interaction between groups, if any. Our scheme can simultaneously select useful groups as well as learn the underlying system. The level of redundancy among groups can also be controlled.en_US
dc.language.isoen_USen_US
dc.subjectSensor selectionen_US
dc.subjectfeature selectionen_US
dc.subjectneural networksen_US
dc.subjectredundancy controlen_US
dc.titleSENSOR (GROUP FEATURE) SELECTION WITH CONTROLLED REDUNDANCY IN A CONNECTIONIST FRAMEWORKen_US
dc.typeArticleen_US
dc.identifier.doi10.1142/S012906571450021Xen_US
dc.identifier.journalINTERNATIONAL JOURNAL OF NEURAL SYSTEMSen_US
dc.citation.volume24en_US
dc.citation.issue6en_US
dc.citation.epageen_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000340463300005-
dc.citation.woscount0-
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