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Density-based averaging - a new operator for data fusion

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Density-based averaging - a new operator for data fusion. / Angelov, Plamen; Yager, Ronald.
In: Information Sciences, Vol. 222, 10.02.2013, p. 163-174.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Angelov, P & Yager, R 2013, 'Density-based averaging - a new operator for data fusion', Information Sciences, vol. 222, pp. 163-174. https://doi.org/10.1016/j.ins.2012.08.006

APA

Angelov, P., & Yager, R. (2013). Density-based averaging - a new operator for data fusion. Information Sciences, 222, 163-174. https://doi.org/10.1016/j.ins.2012.08.006

Vancouver

Angelov P, Yager R. Density-based averaging - a new operator for data fusion. Information Sciences. 2013 Feb 10;222:163-174. Epub 2012 Sept 7. doi: 10.1016/j.ins.2012.08.006

Author

Angelov, Plamen ; Yager, Ronald. / Density-based averaging - a new operator for data fusion. In: Information Sciences. 2013 ; Vol. 222. pp. 163-174.

Bibtex

@article{358bcf9f0f974dd880a11c35ab910a3d,
title = "Density-based averaging - a new operator for data fusion",
abstract = "A new data fusion operator based on averaging that is weighted by the density of each particular data sample is introduced in this paper. The proposed approach differs from other weighted averages by its suitability to on-line, real-time applications due to the fact that recursive calculations are being used. It alsodiffers by the fact that it is non-parametric. The proposed operator has a very wide area of possible applications same as the traditional average and most of the other weighted averages. This includes, but is not limited to clustering, classification, pattern recognition, group decision making approaches, datafusion, etc. Some illustrative numerical examples are provided mainly as a proof of concept, including its application to classification. Two simple, yet very effective classification approaches based on the density-based weights called {\textquoteleft}one-rule-per-class{\textquoteright} or 1R/C and on the minimum distance to weighted class mean has been introduced. Further work will focus on more application-oriented studies that cover various practical applications to clustering and use of different distance measures.",
keywords = "weighted averages, data fusion, data density, Cauchy kernel, Fuzzy classifiers",
author = "Plamen Angelov and Ronald Yager",
year = "2013",
month = feb,
day = "10",
doi = "10.1016/j.ins.2012.08.006",
language = "English",
volume = "222",
pages = "163--174",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Density-based averaging - a new operator for data fusion

AU - Angelov, Plamen

AU - Yager, Ronald

PY - 2013/2/10

Y1 - 2013/2/10

N2 - A new data fusion operator based on averaging that is weighted by the density of each particular data sample is introduced in this paper. The proposed approach differs from other weighted averages by its suitability to on-line, real-time applications due to the fact that recursive calculations are being used. It alsodiffers by the fact that it is non-parametric. The proposed operator has a very wide area of possible applications same as the traditional average and most of the other weighted averages. This includes, but is not limited to clustering, classification, pattern recognition, group decision making approaches, datafusion, etc. Some illustrative numerical examples are provided mainly as a proof of concept, including its application to classification. Two simple, yet very effective classification approaches based on the density-based weights called ‘one-rule-per-class’ or 1R/C and on the minimum distance to weighted class mean has been introduced. Further work will focus on more application-oriented studies that cover various practical applications to clustering and use of different distance measures.

AB - A new data fusion operator based on averaging that is weighted by the density of each particular data sample is introduced in this paper. The proposed approach differs from other weighted averages by its suitability to on-line, real-time applications due to the fact that recursive calculations are being used. It alsodiffers by the fact that it is non-parametric. The proposed operator has a very wide area of possible applications same as the traditional average and most of the other weighted averages. This includes, but is not limited to clustering, classification, pattern recognition, group decision making approaches, datafusion, etc. Some illustrative numerical examples are provided mainly as a proof of concept, including its application to classification. Two simple, yet very effective classification approaches based on the density-based weights called ‘one-rule-per-class’ or 1R/C and on the minimum distance to weighted class mean has been introduced. Further work will focus on more application-oriented studies that cover various practical applications to clustering and use of different distance measures.

KW - weighted averages

KW - data fusion

KW - data density

KW - Cauchy kernel

KW - Fuzzy classifiers

U2 - 10.1016/j.ins.2012.08.006

DO - 10.1016/j.ins.2012.08.006

M3 - Journal article

VL - 222

SP - 163

EP - 174

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -