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Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning
นักวิจัย : Verma, Brijesh. , Rahman, Ashfaqur.
คำค้น : Cluster analysis. , Applied research. , 890202 Application Tools and System Utilities. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080109 Pattern Recognition and Data Mining. , Classifying spaces. , Cluster set theory. , Ensemble classifier -- Clustering -- Classification -- Fusion of classifiers
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2554
อ้างอิง : http://hdl.cqu.edu.au/10018/55234
ที่มา : Verma, B & Rahman, A 2011, 'Cluster oriented ensemble classifier: impact of multi-cluster characterisation on ensemble classifier learning', IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 4, pp. 605-618, http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.28
ความเชี่ยวชาญ : -
ความสัมพันธ์ : IEEE transactions on knowledge and data engineering. U.S.A. : IEEE, 2011. Vol. 24, no. 4 (April 2012), p. 605-618 14 pages Refereed 1041-4347 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

This paper presents a novel cluster oriented ensemble classifier. The proposed ensemble classifier is based on original concepts such as learning of cluster boundaries by the base classifiers and mapping of cluster confidences to class decision using a fusion classifier. The categorised data set is characterised into multiple clusters and fed to a number of distinctive base classifiers. The base classifiers learn cluster boundaries and produce cluster confidence vectors. A second level fusion classifier combines the cluster confidences and maps to class decisions. The proposed ensemble classifier modifies the learning domain for the base classifiers and facilitates efficient learning. The proposed approach is evaluated on benchmark data sets from UCI machine learning repository to identify the impact of multi-cluster boundaries on classifier learning and classification accuracy. The experimental results and two–tailed sign test demonstrate the superiority of the proposed cluster oriented ensemble classifier over existing ensemble classifiers published in the literature.

บรรณานุกรม :
Verma, Brijesh. , Rahman, Ashfaqur. . (2554). Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Rahman, Ashfaqur. . 2554. "Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Rahman, Ashfaqur. . "Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2554. Print.
Verma, Brijesh. , Rahman, Ashfaqur. . Cluster-oriented ensemble classifier : impact of multi-cluster characterisation on ensemble classifier learning. กรุงเทพมหานคร : Central Queensland University, Australia; 2554.