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http://repositorio.inesctec.pt/handle/123456789/3623
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Salisu Mamman Abdulrhaman | en |
dc.contributor.author | Pavel Brazdil | en |
dc.date.accessioned | 2017-11-20T10:49:00Z | - |
dc.date.available | 2017-11-20T10:49:00Z | - |
dc.date.issued | 2014 | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/3623 | - |
dc.description.abstract | The vast majority of studies in meta-learning uses only few performance measures when characterizing different machine learning algorithms. The measure Adjusted Ratios of Ratio (ARR) addresses the problem of how to evaluate the quality of a model based on the accuracy and training time. Unfortunately, this measure suffers from a shortcoming that is described in this paper. A new solution is proposed and it is shown that the proposed function satisfies the criterion of monotonicity, unlike ARR. | en |
dc.language | eng | en |
dc.relation | 6144 | en |
dc.relation | 5339 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Measures for Combining Accuracy and Time for Meta-learning | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
Appears in Collections: | LIAAD - Articles in International Conferences |
Files in This Item:
File | Description | Size | Format | |
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P-009-WT9.pdf | 159.44 kB | Adobe PDF | View/Open |
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