Comparing Datasets by Attribute Alignment

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10284440" target="_blank" >RIV/00216208:11320/14:10284440 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7008148" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=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>

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 attributesof 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 findsuch 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 baselin
Czech name
—
Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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