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Cluster based ensemble classifier generation by joint optimization of accuracy and diversity

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Cluster based ensemble classifier generation by joint optimization of accuracy and diversity
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 classifiers -- Genetic algorithms -- Multi-objective optimization , Journal Article. Refereed, Scholarly Journal
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2556
อ้างอิง : http://hdl.cqu.edu.au/10018/1014876
ที่มา : Rahman, A & Verma, B 2013, 'Cluster based ensemble classifier generation by joint optimization of accuracy and diversity', International Journal of Computational Intelligence and Applications, vol. 12, no. 4, pp. 1340003-1-1340003-13, http://dx.doi.org/10.1142/S1469026813400038
ความเชี่ยวชาญ : -
ความสัมพันธ์ : International journal of computational intelligence and applications. United Kingdom : Imperial College Press, 2013. Vol. 12, no. 4 (2013), p. 1340003-1-1340003-13 12060011 pages Refereed 1469-0268 1757-5885 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

This paper presents an algorithm to generate ensemble classifier by joint optimization of accuracy and diversity. It is expected that the base classifiers in an ensemble are accurate and diverse (i.e. complementary in terms of errors) among each other for the ensemble classifier to be more accurate. We adopt a Multi–Objective Evolutionary Algorithm (MOEA) for joint optimization of accuracy and diversity on our recently developed Non–Uniform Layered Cluster Oriented Ensemble Classifier (NULCOEC). In 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 of the number of layers and clusters. 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 obtained the results of single objective optimization (i.e. optimizing either accuracy or diversity) and compared them with the results of MOEA on sixteen UCI data sets. The results show that the MOEA can improve the performance of ensemble classifier.

บรรณานุกรม :
Rahman, Ashfaqur. , Verma, Brijesh. . (2556). Cluster based ensemble classifier generation by joint optimization of accuracy and diversity.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . 2556. "Cluster based ensemble classifier generation by joint optimization of accuracy and diversity".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . "Cluster based ensemble classifier generation by joint optimization of accuracy and diversity."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2556. Print.
Rahman, Ashfaqur. , Verma, Brijesh. . Cluster based ensemble classifier generation by joint optimization of accuracy and diversity. กรุงเทพมหานคร : Central Queensland University, Australia; 2556.