Distance Estimation for Incomplete Data by Extreme Learning Machine

Emil Eirola; Anton Akusok; Kaj-Mikael Björk; Amaury Lendasse

doi:10.1007/978-3-030-01520-6_18

Distance Estimation for Incomplete Data by Extreme Learning Machine

Emil Eirola, Anton Akusok, Kaj-Mikael Björk, Amaury Lendasse

Finansiell ekonomi, Helsingfors

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.

Originalspråk	Engelska
Titel på värdpublikation	International Conference on Extreme Learning Machine: Proceedings of ELM-2017
Antal sidor	7
Utgivningsort	Cham
Förlag	Springer
Utgivningsdatum	17.10.2018
Sidor	203-209
ISBN (tryckt)	978-3-030-01519-0
ISBN (elektroniskt)	978-3-030-01520-6
DOI	https://doi.org/10.1007/978-3-030-01520-6_18
Status	Publicerad - 17.10.2018
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, Kina Varaktighet: 04.10.2017 → 07.10.2017 http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html

Publikationsserier

Namn	Proceedings in Adaptation, Learning and Optimization (PALO)
Volym	10

Nyckelord

512 Företagsekonomi

Åtkomst av dokument

10.1007/978-3-030-01520-6_18

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@inproceedings{c96eba0568ef4e0cb6bafc3f07ff18ac,

title = "Distance Estimation for Incomplete Data by Extreme Learning Machine",

abstract = "Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.",

keywords = "512 Business and Management, Machine learning, Extreme learning machine, Incomplete data, Missing values, Distance estimation",

author = "Emil Eirola and Anton Akusok and Kaj-Mikael Bj{\"o}rk and Amaury Lendasse",

year = "2018",

month = oct,

day = "17",

doi = "10.1007/978-3-030-01520-6_18",

language = "English",

isbn = "978-3-030-01519-0",

series = "Proceedings in Adaptation, Learning and Optimization (PALO)",

publisher = "Springer",

pages = "203--209",

booktitle = "International Conference on Extreme Learning Machine: Proceedings of ELM-2017",

address = "International",

note = "2017 the 8th International Conference on Extreme Learning Machines (ELM) , ELM-2017 ; Conference date: 04-10-2017 Through 07-10-2017",

url = "http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html",

}

Eirola, E, Akusok, A, Björk, K-M & Lendasse, A 2018, Distance Estimation for Incomplete Data by Extreme Learning Machine. i International Conference on Extreme Learning Machine: Proceedings of ELM-2017. Proceedings in Adaptation, Learning and Optimization (PALO), vol. 10, Springer, Cham, s. 203-209, 2017 the 8th International Conference on Extreme Learning Machines (ELM) , Yantai, Kina, 04.10.2017. https://doi.org/10.1007/978-3-030-01520-6_18

Distance Estimation for Incomplete Data by Extreme Learning Machine. / Eirola, Emil; Akusok, Anton; Björk, Kaj-Mikael et al.
International Conference on Extreme Learning Machine: Proceedings of ELM-2017. Cham: Springer, 2018. s. 203-209 (Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 10).

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

TY - GEN

T1 - Distance Estimation for Incomplete Data by Extreme Learning Machine

AU - Eirola, Emil

AU - Akusok, Anton

AU - Björk, Kaj-Mikael

AU - Lendasse, Amaury

PY - 2018/10/17

Y1 - 2018/10/17

N2 - Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.

AB - Data with missing values are very common in practice, yet many machine learning models are not designed to handle incomplete data. As most machine learning approaches can be formulated in terms of distance between samples, estimating these distances on data with missing values provides an effective way to use such models. This paper present a procedure to estimate the distances using the Extreme Learning Machine. Experimental comparison shows that the proposed approach achieves competitive accuracy with other methods on standard benchmark datasets.

KW - 512 Business and Management

KW - Machine learning

KW - Extreme learning machine

KW - Incomplete data

KW - Missing values

KW - Distance estimation

U2 - 10.1007/978-3-030-01520-6_18

DO - 10.1007/978-3-030-01520-6_18

M3 - Conference contribution

SN - 978-3-030-01519-0

T3 - Proceedings in Adaptation, Learning and Optimization (PALO)

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EP - 209

BT - International Conference on Extreme Learning Machine: Proceedings of ELM-2017

PB - Springer

CY - Cham

T2 - 2017 the 8th International Conference on Extreme Learning Machines (ELM)

Y2 - 4 October 2017 through 7 October 2017

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Distance Estimation for Incomplete Data by Extreme Learning Machine

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