Investigating the effectiveness of conditional classification: an application to manufacturing scheduling
This paper examines the problem of multidimensional classification, an automated learning process where "rules" are to be inferred on separate but related aspects of a problem, using identical or overlapping data sets. A general framework describing the various types of multidimensional cl...
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| Veröffentlicht in: | IEEE transactions on engineering management 1994-05, Vol.41 (2), p.183-193 |
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| container_title | IEEE transactions on engineering management |
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| creator | Chaturvedi, A.R. Nazareth, D.L. |
| description | This paper examines the problem of multidimensional classification, an automated learning process where "rules" are to be inferred on separate but related aspects of a problem, using identical or overlapping data sets. A general framework describing the various types of multidimensional classification is provided. The paper specifically concentrates on conditional classification, wherein the order of classification is based on domain semantics. Drawing from concept learning and information theory, algorithms are presented for acquiring tree-structured knowledge from available data. An application to manufacturing scheduling is presented. Results indicate that conditional classification may provide some ability to better interpret related decisions in automated manufacturing contexts. Further work is necessary to ascertain if the approach is robust, particularly on more complex decisions, larger data sets, and noisy data.< > |
| doi_str_mv | 10.1109/17.293385 |
| format | Article |
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Further work is necessary to ascertain if the approach is robust, particularly on more complex decisions, larger data sets, and noisy data.< ></description><subject>Applied sciences</subject><subject>Automation</subject><subject>Availability</subject><subject>Classification</subject><subject>Data mining</subject><subject>Decision trees</subject><subject>Effectiveness</subject><subject>Exact sciences and technology</subject><subject>Expert systems</subject><subject>Humans</subject><subject>Job shop scheduling</subject><subject>Machine learning</subject><subject>Manufacturing</subject><subject>Manufacturing automation</subject><subject>Manufacturing processes</subject><subject>Mathematical models</subject><subject>Operational research and scientific management</subject><subject>Operational research. 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| identifier | ISSN: 0018-9391 |
| ispartof | IEEE transactions on engineering management, 1994-05, Vol.41 (2), p.183-193 |
| issn | 0018-9391 1558-0040 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_3349190 |
| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Automation Availability Classification Data mining Decision trees Effectiveness Exact sciences and technology Expert systems Humans Job shop scheduling Machine learning Manufacturing Manufacturing automation Manufacturing processes Mathematical models Operational research and scientific management Operational research. Management science Pulp manufacturing Robustness Scheduling Scheduling, sequencing Studies |
| title | Investigating the effectiveness of conditional classification: an application to manufacturing scheduling |
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