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

Finance, Helsinki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	International Conference on Extreme Learning Machine: Proceedings of ELM-2017
Number of pages	7
Place of Publication	Cham
Publisher	Springer
Publication date	17.10.2018
Pages	203-209
ISBN (Print)	978-3-030-01519-0
ISBN (Electronic)	978-3-030-01520-6
DOIs	https://doi.org/10.1007/978-3-030-01520-6_18
Publication status	Published - 17.10.2018
MoE publication type	A4 Article in conference proceedings
Event	2017 the 8th International Conference on Extreme Learning Machines (ELM) - Yantai, China Duration: 04.10.2017 → 07.10.2017 http://www.ntu.edu.sg/home/egbhuang/elm2017/index.html

Publication series

Name	Proceedings in Adaptation, Learning and Optimization (PALO)
Volume	10

Keywords

512 Business and Management
Machine learning
Extreme learning machine
Incomplete data
Missing values
Distance estimation

Access to Document

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

OpenUrl availability

Full text

Cite this

@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. in International Conference on Extreme Learning Machine: Proceedings of ELM-2017. Proceedings in Adaptation, Learning and Optimization (PALO), vol. 10, Springer, Cham, pp. 203-209, 2017 the 8th International Conference on Extreme Learning Machines (ELM) , Yantai, China, 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. p. 203-209 (Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 10).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › 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)

SP - 203

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

ER -

Distance Estimation for Incomplete Data by Extreme Learning Machine

Abstract

Publication series

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this