Feature Extraction and Aggregation for Predicting the Euro 2016

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

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Feature Extraction and Aggregation for Predicting the Euro 2016. / Tavakol, Maryam; Zafartavanaelmi, Hamid; Brefeld, Ulf.
in: CEUR Workshop Proceedings, Jahrgang 1842, Nr. 1842, 09.2016.

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

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@article{85cd4fedd3a54bd98cc5aea145889fe1,
title = "Feature Extraction and Aggregation for Predicting the Euro 2016",
abstract = "This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.",
keywords = "Business informatics, Feature extraction, ridge regression , ranking",
author = "Maryam Tavakol and Hamid Zafartavanaelmi and Ulf Brefeld",
note = "Session 1. urn:nbn:de:0074-1842-7; Machine Learning and Data Mining for Sports Analytics - MLSA 2016 : ECML/PKDD 2016 workshop, MLSA 2016 ; Conference date: 19-09-2016",
year = "2016",
month = sep,
language = "English",
volume = "1842",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "Rheinisch-Westfaelische Technische Hochschule Aachen",
number = "1842",
url = "https://dtai.cs.kuleuven.be/events/MLSA16/",

}

RIS

TY - JOUR

T1 - Feature Extraction and Aggregation for Predicting the Euro 2016

AU - Tavakol, Maryam

AU - Zafartavanaelmi, Hamid

AU - Brefeld, Ulf

N1 - Conference code: 3

PY - 2016/9

Y1 - 2016/9

N2 - This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.

AB - This paper is addressing the challenge of predicting Euro 2016 outcomes. A set of processed features alongside with a new proposed feature are used to train a linear model to compute scores of 24 participating countries. The obtained scores form fwin, lose, drawg probabilities for all possible fixtures. The empirical evaluation until the semi-finals shows that the conceptually simple approach proves accurate for countries with historical data.

KW - Business informatics

KW - Feature extraction

KW - ridge regression

KW - ranking

UR - http://ceur-ws.org/Vol-1842/

UR - http://www.scopus.com/inward/record.url?scp=85020500932&partnerID=8YFLogxK

M3 - Conference article in journal

VL - 1842

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

IS - 1842

T2 - Machine Learning and Data Mining for Sports Analytics - MLSA 2016

Y2 - 19 September 2016

ER -

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