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Applications of Autonomous Data Partitioning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Applications of Autonomous Data Partitioning. / Angelov, P.P.; Gu, X.
Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 261-276 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Angelov, PP & Gu, X 2019, Applications of Autonomous Data Partitioning. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 261-276. https://doi.org/10.1007/978-3-030-02384-3_11

APA

Angelov, P. P., & Gu, X. (2019). Applications of Autonomous Data Partitioning. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 261-276). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_11

Vancouver

Angelov PP, Gu X. Applications of Autonomous Data Partitioning. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 261-276. (Studies in Computational Intelligence). Epub 2018 Oct 18. doi: 10.1007/978-3-030-02384-3_11

Author

Angelov, P.P. ; Gu, X. / Applications of Autonomous Data Partitioning. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 261-276 (Studies in Computational Intelligence).

Bibtex

@inbook{070bee9c12e24f8887d38eeddf10b080,
title = "Applications of Autonomous Data Partitioning",
abstract = "In this chapter, the algorithm summaries of both, the offline and evolving versions of the proposed autonomous data partitioning (ADP) algorithm described in chapter 7 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the performance of the ADP algorithm on data partitioning. Furthermore, numerical examples on semi-supervised classification are also conducted as a potential application of the ADP algorithm. The state-of-the-art approaches are used for comparison. Numerical experiments demonstrate that the ADP algorithm is able to perform high quality data partitioning results in a highly efficient, objective manner. The ADP algorithm can also be used for classification even when there is very little supervision available. The pseudo-code of the main procedure of the ADP algorithm and the MATLAB implementations can be found in appendices B.2 and C.2, respectively. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_11",
language = "English",
isbn = "9783030023836)",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "261--276",
editor = "Plamen Angelov and Gu, {Xiaowei }",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Applications of Autonomous Data Partitioning

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - In this chapter, the algorithm summaries of both, the offline and evolving versions of the proposed autonomous data partitioning (ADP) algorithm described in chapter 7 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the performance of the ADP algorithm on data partitioning. Furthermore, numerical examples on semi-supervised classification are also conducted as a potential application of the ADP algorithm. The state-of-the-art approaches are used for comparison. Numerical experiments demonstrate that the ADP algorithm is able to perform high quality data partitioning results in a highly efficient, objective manner. The ADP algorithm can also be used for classification even when there is very little supervision available. The pseudo-code of the main procedure of the ADP algorithm and the MATLAB implementations can be found in appendices B.2 and C.2, respectively. © 2019, Springer Nature Switzerland AG.

AB - In this chapter, the algorithm summaries of both, the offline and evolving versions of the proposed autonomous data partitioning (ADP) algorithm described in chapter 7 are provided. Numerical examples based on well-known benchmark datasets are presented for evaluating the performance of the ADP algorithm on data partitioning. Furthermore, numerical examples on semi-supervised classification are also conducted as a potential application of the ADP algorithm. The state-of-the-art approaches are used for comparison. Numerical experiments demonstrate that the ADP algorithm is able to perform high quality data partitioning results in a highly efficient, objective manner. The ADP algorithm can also be used for classification even when there is very little supervision available. The pseudo-code of the main procedure of the ADP algorithm and the MATLAB implementations can be found in appendices B.2 and C.2, respectively. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_11

DO - 10.1007/978-3-030-02384-3_11

M3 - Chapter (peer-reviewed)

SN - 9783030023836)

VL - 800

T3 - Studies in Computational Intelligence

SP - 261

EP - 276

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -