Linear goal programming in estimation of classification probability
Recently, linear programming approaches to classification problems have been widely studied and have been shown to yield satisfactory results. However, those linear programming model do not incorporate discriminating information about objects within groups. In order to utilize within group variation...
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Veröffentlicht in: | European journal of operational research 1993-05, Vol.67 (1), p.101-110 |
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container_title | European journal of operational research |
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creator | Lam, K.F. Choo, E.U. Wedley, W.C. |
description | Recently, linear programming approaches to classification problems have been widely studied and have been shown to yield satisfactory results. However, those linear programming model do not incorporate discriminating information about objects within groups. In order to utilize within group variations, an approach is presented which directly incorporates membership probabilities in a linear goal programming model. |
doi_str_mv | 10.1016/0377-2217(93)90325-H |
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However, those linear programming model do not incorporate discriminating information about objects within groups. In order to utilize within group variations, an approach is presented which directly incorporates membership probabilities in a linear goal programming model.</description><subject>Applied sciences</subject><subject>Classification</subject><subject>Decision making</subject><subject>Decision theory. Utility theory</subject><subject>Exact sciences and technology</subject><subject>Goal programming</subject><subject>Linear programming</subject><subject>Multiple criteria decision-making</subject><subject>Multivariate statistics</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Operations research</subject><subject>Statistical analysis</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UE2LFDEQDaLguPoPPDQi4h56Nx-dpHMRlsF1XAb2oudQnamMGbo7Y9KzMP_etL3MwYOBSoXw3qtXj5D3jN4wytQtFVrXnDP92YhrQwWX9eYFWbFW81q1ir4kqwvkNXmT84FSyiSTK7LehhEhVfsIfXVMcZ9gGMK4r8JYYZ7CAFOIYxV95XrIOfjglp-C7aALfZjOb8krD33Gd8_9ivy8__pjvam3j9--r--2tWtkO9VSCdBoOG3NDqVDuTPcSN-wjncOQWjwrWe-Mayjrlh3ICQ3tFUaOg-mEVfk06JbZv8-FXd2CNlh38OI8ZQtV1QrI2gBfvgHeIinNBZvltOGcWWYLKBmAbkUc07o7TGVddPZMmrnWO2cmZ0zs0bYv7HaTaE9LLSER3QXDpZziAmzfbIClC7XuRQzhSogzM9Sx7lTZlkZ8WsaitjHZ6OQHfQ-wehCvog2msm2mff5ssCwpPsUMNnsAo4OdyGhm-wuhv-b_gNZXaRH</recordid><startdate>19930528</startdate><enddate>19930528</enddate><creator>Lam, K.F.</creator><creator>Choo, E.U.</creator><creator>Wedley, W.C.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19930528</creationdate><title>Linear goal programming in estimation of classification probability</title><author>Lam, K.F. ; Choo, E.U. ; Wedley, W.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c458t-563a7e92089de5ce5d9295f41b2bcea37af8f1f491b0c872ca35290867abfa943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Applied sciences</topic><topic>Classification</topic><topic>Decision making</topic><topic>Decision theory. 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source | RePEc; Elsevier ScienceDirect Journals |
subjects | Applied sciences Classification Decision making Decision theory. Utility theory Exact sciences and technology Goal programming Linear programming Multiple criteria decision-making Multivariate statistics Operational research and scientific management Operational research. Management science Operations research Statistical analysis |
title | Linear goal programming in estimation of classification probability |
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