A cross-selection instance algorithm

Junhai ZHAI; Ta LI; Xizhao WANG

doi:10.3233/IFS-151792

A cross-selection instance algorithm

Junhai ZHAI^*, Ta LI, Xizhao WANG

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

16 Citations (Scopus)

Abstract

Motivated by the idea of cross-validation, a novel instance selection algorithm is proposed in this paper. The novelties of the proposed algorithm are that (1) it cross selects the important instances from the original data set with a committee, (2) it can deal with the problem of selecting instance from large data sets. We experimentally compared our algorithm with five state-of-the-art approaches which are CNN, ENN, RNN, MCS, and ICF on 3 artificial data sets and 6 UCI data sets, including 4 large data sets, ranking from 130K to 4898K in size. The experimental results show that the proposed algorithm is very efficient and effective, especially on large data sets.

Original language	English
Pages (from-to)	717-728
Number of pages	12
Journal	Journal of Intelligent and Fuzzy Systems
Volume	30
Issue number	2
DOIs	https://doi.org/10.3233/IFS-151792
Publication status	Published - 9 Feb 2016
Externally published	Yes

Bibliographical note

This research is supported by the national natural science foundation of China (61170040, 71371063), by the natural science foundation of Hebei Province (F2013201110, F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD20131028) and by the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University, China.

Keywords

extreme learning machine
Instances selection
K-L divergence
large data sets

Access to Document

10.3233/IFS-151792

Cite this

@article{8cebe035b66c4572bf4e028f273ee379,

title = "A cross-selection instance algorithm",

abstract = "Motivated by the idea of cross-validation, a novel instance selection algorithm is proposed in this paper. The novelties of the proposed algorithm are that (1) it cross selects the important instances from the original data set with a committee, (2) it can deal with the problem of selecting instance from large data sets. We experimentally compared our algorithm with five state-of-the-art approaches which are CNN, ENN, RNN, MCS, and ICF on 3 artificial data sets and 6 UCI data sets, including 4 large data sets, ranking from 130K to 4898K in size. The experimental results show that the proposed algorithm is very efficient and effective, especially on large data sets.",

keywords = "extreme learning machine, Instances selection, K-L divergence, large data sets",

author = "Junhai ZHAI and Ta LI and Xizhao WANG",

note = "This research is supported by the national natural science foundation of China (61170040, 71371063), by the natural science foundation of Hebei Province (F2013201110, F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD20131028) and by the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University, China.",

year = "2016",

month = feb,

day = "9",

doi = "10.3233/IFS-151792",

language = "English",

volume = "30",

pages = "717--728",

journal = "Journal of Intelligent and Fuzzy Systems",

issn = "1064-1246",

publisher = "IOS Press",

number = "2",

}

TY - JOUR

T1 - A cross-selection instance algorithm

AU - ZHAI, Junhai

AU - LI, Ta

AU - WANG, Xizhao

N1 - This research is supported by the national natural science foundation of China (61170040, 71371063), by the natural science foundation of Hebei Province (F2013201110, F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD20131028) and by the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University, China.

PY - 2016/2/9

Y1 - 2016/2/9

N2 - Motivated by the idea of cross-validation, a novel instance selection algorithm is proposed in this paper. The novelties of the proposed algorithm are that (1) it cross selects the important instances from the original data set with a committee, (2) it can deal with the problem of selecting instance from large data sets. We experimentally compared our algorithm with five state-of-the-art approaches which are CNN, ENN, RNN, MCS, and ICF on 3 artificial data sets and 6 UCI data sets, including 4 large data sets, ranking from 130K to 4898K in size. The experimental results show that the proposed algorithm is very efficient and effective, especially on large data sets.

AB - Motivated by the idea of cross-validation, a novel instance selection algorithm is proposed in this paper. The novelties of the proposed algorithm are that (1) it cross selects the important instances from the original data set with a committee, (2) it can deal with the problem of selecting instance from large data sets. We experimentally compared our algorithm with five state-of-the-art approaches which are CNN, ENN, RNN, MCS, and ICF on 3 artificial data sets and 6 UCI data sets, including 4 large data sets, ranking from 130K to 4898K in size. The experimental results show that the proposed algorithm is very efficient and effective, especially on large data sets.

KW - extreme learning machine

KW - Instances selection

KW - K-L divergence

KW - large data sets

UR - http://www.scopus.com/inward/record.url?scp=84958673983&partnerID=8YFLogxK

U2 - 10.3233/IFS-151792

DO - 10.3233/IFS-151792

M3 - Journal Article (refereed)

AN - SCOPUS:84958673983

SN - 1064-1246

VL - 30

SP - 717

EP - 728

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

IS - 2

ER -

A cross-selection instance algorithm

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this