A novel dynamic rough subspace based selective ensemble

Research output: Contribution to journalArticlepeer-review

Standard

A novel dynamic rough subspace based selective ensemble. / Guo, Yuwei; Jiao, Licheng; Wang, Shuang; Wang, Shuo; Liu, Fang; Rong, Kaixuan; Xiong, Tao.

In: Pattern Recognition, Vol. 48, No. 5, 01.05.2015, p. 1638-1652.

Research output: Contribution to journalArticlepeer-review

Harvard

Guo, Y, Jiao, L, Wang, S, Wang, S, Liu, F, Rong, K & Xiong, T 2015, 'A novel dynamic rough subspace based selective ensemble', Pattern Recognition, vol. 48, no. 5, pp. 1638-1652. https://doi.org/10.1016/j.patcog.2014.11.001

APA

Guo, Y., Jiao, L., Wang, S., Wang, S., Liu, F., Rong, K., & Xiong, T. (2015). A novel dynamic rough subspace based selective ensemble. Pattern Recognition, 48(5), 1638-1652. https://doi.org/10.1016/j.patcog.2014.11.001

Vancouver

Author

Guo, Yuwei ; Jiao, Licheng ; Wang, Shuang ; Wang, Shuo ; Liu, Fang ; Rong, Kaixuan ; Xiong, Tao. / A novel dynamic rough subspace based selective ensemble. In: Pattern Recognition. 2015 ; Vol. 48, No. 5. pp. 1638-1652.

Bibtex

@article{566e6b8c1eb74f22b8384104b4bbeab7,
title = "A novel dynamic rough subspace based selective ensemble",
abstract = "Ensemble learning has been a hot topic in machine learning due to its successful utilization in many applications. Rough set theory has been proved to be an excellent mathematical tool for dimension reduction. In this paper, based on rough set, a novel framework for ensemble is proposed. In our proposed framework, the relationship among attributes in rough subspace is first considered, and the maximum dependency degree of attribute is first employed to effectively reduce the searching space of reducts and augment the diversity of selected reducts. In addition, in order to choose an appropriate reduct from the dynamic reduct searching space, an assessment function which can balance the accuracy and diversity is utilized. At last, a new method, i.e., Dynamic Rough Subspace based Selective Ensemble (DRSSE), which is derived from our framework is given. By repeatedly changing the searching space of reducts and selecting the next reduct from the changed searching space, DRSSE finally trains an ensemble system with these selected reducts. Compared with several available ensemble methods, experimental results with several datasets demonstrate that DRSSE can lead to a comparative or even better performance.",
author = "Yuwei Guo and Licheng Jiao and Shuang Wang and Shuo Wang and Fang Liu and Kaixuan Rong and Tao Xiong",
year = "2015",
month = may,
day = "1",
doi = "10.1016/j.patcog.2014.11.001",
language = "English",
volume = "48",
pages = "1638--1652",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier",
number = "5",

}

RIS

TY - JOUR

T1 - A novel dynamic rough subspace based selective ensemble

AU - Guo, Yuwei

AU - Jiao, Licheng

AU - Wang, Shuang

AU - Wang, Shuo

AU - Liu, Fang

AU - Rong, Kaixuan

AU - Xiong, Tao

PY - 2015/5/1

Y1 - 2015/5/1

N2 - Ensemble learning has been a hot topic in machine learning due to its successful utilization in many applications. Rough set theory has been proved to be an excellent mathematical tool for dimension reduction. In this paper, based on rough set, a novel framework for ensemble is proposed. In our proposed framework, the relationship among attributes in rough subspace is first considered, and the maximum dependency degree of attribute is first employed to effectively reduce the searching space of reducts and augment the diversity of selected reducts. In addition, in order to choose an appropriate reduct from the dynamic reduct searching space, an assessment function which can balance the accuracy and diversity is utilized. At last, a new method, i.e., Dynamic Rough Subspace based Selective Ensemble (DRSSE), which is derived from our framework is given. By repeatedly changing the searching space of reducts and selecting the next reduct from the changed searching space, DRSSE finally trains an ensemble system with these selected reducts. Compared with several available ensemble methods, experimental results with several datasets demonstrate that DRSSE can lead to a comparative or even better performance.

AB - Ensemble learning has been a hot topic in machine learning due to its successful utilization in many applications. Rough set theory has been proved to be an excellent mathematical tool for dimension reduction. In this paper, based on rough set, a novel framework for ensemble is proposed. In our proposed framework, the relationship among attributes in rough subspace is first considered, and the maximum dependency degree of attribute is first employed to effectively reduce the searching space of reducts and augment the diversity of selected reducts. In addition, in order to choose an appropriate reduct from the dynamic reduct searching space, an assessment function which can balance the accuracy and diversity is utilized. At last, a new method, i.e., Dynamic Rough Subspace based Selective Ensemble (DRSSE), which is derived from our framework is given. By repeatedly changing the searching space of reducts and selecting the next reduct from the changed searching space, DRSSE finally trains an ensemble system with these selected reducts. Compared with several available ensemble methods, experimental results with several datasets demonstrate that DRSSE can lead to a comparative or even better performance.

U2 - 10.1016/j.patcog.2014.11.001

DO - 10.1016/j.patcog.2014.11.001

M3 - Article

VL - 48

SP - 1638

EP - 1652

JO - Pattern Recognition

JF - Pattern Recognition

SN - 0031-3203

IS - 5

ER -