Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Vasily Sachnev; Hyoung Joong Kim

doi:10.1109/ISSNIP.2014.6827649

Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Vasily Sachnev, Hyoung Joong Kim

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

9 Citations (Scopus)

Abstract

In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

Original language	English
Title of host publication	IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
Publisher	IEEE Computer Society
ISBN (Print)	9781479928439
DOIs	https://doi.org/10.1109/ISSNIP.2014.6827649
Publication status	Published - 2014
Event	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore Duration: 2014 Apr 21 → 2014 Apr 24

Publication series

Name	IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

Other

Other	9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
Country/Territory	Singapore
City	Singapore
Period	14/4/21 → 14/4/24

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems

Access to Document

10.1109/ISSNIP.2014.6827649

Cite this

Sachnev, V., & Kim, H. J. (2014). Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings Article 6827649 (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). IEEE Computer Society. https://doi.org/10.1109/ISSNIP.2014.6827649

Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. / Sachnev, Vasily; Kim, Hyoung Joong.
IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. 6827649 (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).

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

Sachnev, V & Kim, HJ 2014, Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. in IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings., 6827649, IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, IEEE Computer Society, 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014, Singapore, Singapore, 14/4/21. https://doi.org/10.1109/ISSNIP.2014.6827649

Sachnev V, Kim HJ. Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society. 2014. 6827649. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings). doi: 10.1109/ISSNIP.2014.6827649

Sachnev, Vasily ; Kim, Hyoung Joong. / Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings. IEEE Computer Society, 2014. (IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings).

@inproceedings{3a5cdbe5b59c4a5fa34aa8833b478d93,

title = "Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine",

abstract = "In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.",

author = "Vasily Sachnev and Kim, {Hyoung Joong}",

year = "2014",

doi = "10.1109/ISSNIP.2014.6827649",

language = "English",

isbn = "9781479928439",

series = "IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings",

publisher = "IEEE Computer Society",

booktitle = "IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings",

note = "9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 ; Conference date: 21-04-2014 Through 24-04-2014",

}

TY - GEN

T1 - Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

AU - Sachnev, Vasily

AU - Kim, Hyoung Joong

PY - 2014

Y1 - 2014

N2 - In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

AB - In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

UR - http://www.scopus.com/inward/record.url?scp=84903721396&partnerID=8YFLogxK

U2 - 10.1109/ISSNIP.2014.6827649

DO - 10.1109/ISSNIP.2014.6827649

M3 - Conference contribution

AN - SCOPUS:84903721396

SN - 9781479928439

T3 - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

BT - IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings

PB - IEEE Computer Society

T2 - 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014

Y2 - 21 April 2014 through 24 April 2014

ER -

Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this