Applications of Autonomous Data Partitioning

Angelov, P.P. and Gu, X. (2019) Applications of Autonomous Data Partitioning. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, 800 . Springer-Verlag, pp. 261-276. ISBN 9783030023836)

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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. © 2019, Springer Nature Switzerland AG.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
129542
Deposited By:
Deposited On:
08 Jan 2019 14:40
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:00