Performance evaluation of feature selection algorithms on human activity classification

Gokalp Tulum; N. Tugrul Artug; Bulent Bolat

doi:10.1109/INISTA.2013.6577634

Performance evaluation of feature selection algorithms on human activity classification

Gokalp Tulum, N. Tugrul Artug, Bulent Bolat

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

3 Citations (Scopus)

Abstract

In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.

Original language	English
Title of host publication	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
DOIs	https://doi.org/10.1109/INISTA.2013.6577634
Publication status	Published - 2013
Externally published	Yes
Event	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 - Albena, Bulgaria Duration: 19 Jun 2013 → 21 Jun 2013

Publication series

Name	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

Conference

Conference	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
Country/Territory	Bulgaria
City	Albena
Period	19/06/13 → 21/06/13

Keywords

Feature selection
human activity detection
ReliefF
t-score

Access to Document

10.1109/INISTA.2013.6577634

Cite this

Tulum, G., Artug, N. T., & Bolat, B. (2013). Performance evaluation of feature selection algorithms on human activity classification. In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 Article 6577634 (2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013). https://doi.org/10.1109/INISTA.2013.6577634

@inproceedings{3269f34d3cf04a60920723c31df86750,

title = "Performance evaluation of feature selection algorithms on human activity classification",

abstract = "In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.",

keywords = "Feature selection, human activity detection, ReliefF, t-score",

author = "Gokalp Tulum and Artug, {N. Tugrul} and Bulent Bolat",

year = "2013",

doi = "10.1109/INISTA.2013.6577634",

language = "English",

isbn = "9781479906611",

series = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013",

booktitle = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013",

note = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 ; Conference date: 19-06-2013 Through 21-06-2013",

}

Tulum, G, Artug, NT & Bolat, B 2013, Performance evaluation of feature selection algorithms on human activity classification. in 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013., 6577634, 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, Albena, Bulgaria, 19/06/13. https://doi.org/10.1109/INISTA.2013.6577634

Performance evaluation of feature selection algorithms on human activity classification. / Tulum, Gokalp; Artug, N. Tugrul; Bolat, Bulent.
2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. 2013. 6577634 (2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013).

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

TY - GEN

T1 - Performance evaluation of feature selection algorithms on human activity classification

AU - Tulum, Gokalp

AU - Artug, N. Tugrul

AU - Bolat, Bulent

PY - 2013

Y1 - 2013

N2 - In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.

AB - In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.

KW - Feature selection

KW - human activity detection

KW - ReliefF

KW - t-score

UR - http://www.scopus.com/inward/record.url?scp=84883347100&partnerID=8YFLogxK

U2 - 10.1109/INISTA.2013.6577634

DO - 10.1109/INISTA.2013.6577634

M3 - Conference contribution

AN - SCOPUS:84883347100

SN - 9781479906611

T3 - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

BT - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

T2 - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

Y2 - 19 June 2013 through 21 June 2013

ER -

Tulum G, Artug NT, Bolat B. Performance evaluation of feature selection algorithms on human activity classification. In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. 2013. 6577634. (2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013). doi: 10.1109/INISTA.2013.6577634

Performance evaluation of feature selection algorithms on human activity classification

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this