Comparing Datasets by Attribute Alignment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00462767" target="_blank" >RIV/67985807:_____/14:00462767 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CIDM.2014.7008148" target="_blank" >http://dx.doi.org/10.1109/CIDM.2014.7008148</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CIDM.2014.7008148" target="_blank" >10.1109/CIDM.2014.7008148</a>
Alternative languages
Result language
angličtina
Original language name
Comparing Datasets by Attribute Alignment
Original language description
Metalearning approach to the model selection problem - exploiting the idea that algorithms perform similarly on similar datasets - requires a suitable metric on the dataset space. One common approach compares the datasets based on fixed number of features describing the datasets as a whole. The information based on individual attributes is usually aggregated, taken for the most relevant attributes only, or omitted altogether. In this paper, we propose an approach that aligns complete sets of attributes of the datasets, allowing for different number of attributes. By supplying the distance between two attributes, one can find the alignment minimizing the sum of individual distances between aligned attributes. We present two methods that are able to find such an alignment. They differ in computational complexity and presumptions about the distance function between two attributes supplied. Experiments were performed using the proposed methods and the results were compared with the baseline algorithm.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/LD13002" target="_blank" >LD13002: Modeling of complex systems for softcomputing methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CIDM 2014 IEEE Symposium on Computational Intelligence and Data Mining
ISBN
978-1-4799-4518-4
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
56-62
Publisher name
IEEE
Place of publication
Piscataway
Event location
Orlando
Event date
Dec 9, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000381485400008