Comparing Datasets by Attribute Alignment

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Datasets by Attribute Alignment

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Comparing Datasets by Attribute Alignment

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE CIDM

ISBN

978-1-4799-4519-1

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

56-62

Název nakladatele

IEEE

Místo vydání

Orlando, Fl

Místo konání akce

Orlando

Datum konání akce

9. 12. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—