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Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm
นักวิจัย : Rahman, Ashfaqur. , Verma, Brijesh.
คำค้น : LIBRARY OF CONGRESS NEEDED , Applied research. , 890202 Application Tools and System Utilities. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080109 Pattern Recognition and Data Mining. , Ensemble classifier -- Genetic algorithm -- Multi-objective optimization , Conference Paper. Full Paper (Refereed)
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2556
อ้างอิง : http://hdl.cqu.edu.au/10018/1015918
ที่มา : Rahman, A, Verma, B 2013, 'Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm', The 2013 International Joint Conference on Neural Networks (IJCNN), IEEE, Piscataway, NJ, USA, http://dx.doi.org/10.1109/IJCNN.2013.6706822
ความเชี่ยวชาญ : -
ความสัมพันธ์ : International Joint Conference on Neural Networks (IJCNN 2013), Dallas, Texas, 4-9 August 2013. Piscataway, NJ : IEEE, 2013. p. 1-6 6 pages Refereed 9781467361286 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

In this paper, we present an application of Multi–Objective Evolutionary Algorithm (MOEA) for generating cluster oriented ensemble classifier. In our recently developed Non–Uniform Layered Cluster Oriented Ensemble Classifier (NULCOEC), the data set is partitioned into a variable number of clusters at different layers. Base classifiers are then trained on the clusters at different layers. The performance of NULCOEC is a function of the vector (layers,clusters) and the research presented in this paper investigates the implication of applying MOEA to generate NULCOEC. Accuracy and diversity of the ensemble classifier is expressed as a function of layers and clusters. A MOEA then searches for the combination of layers and clusters to obtain the non–dominated set of (accuracy,diversity). We have also obtained the results of single objective optimization (i.e. optimizing either accuracy or diversity) and compared them with the results of MOEA. The results show that the MOEA can improve the performance of ensemble classifier.

บรรณานุกรม :
Rahman, Ashfaqur. , Verma, Brijesh. . (2556). Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . 2556. "Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . "Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2556. Print.
Rahman, Ashfaqur. , Verma, Brijesh. . Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm. กรุงเทพมหานคร : Central Queensland University, Australia; 2556.