Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters

2008 ◽  
Vol 30 (1) ◽  
pp. 160-173 ◽  
Author(s):  
H.G. Ayad ◽  
M.S. Kamel
Keyword(s):  
Variable Number ◽  
Consensus Method ◽  
Number Of Clusters ◽  
Cumulative Voting

An evolutionary algorithm for clustering data streams with a variable number of clusters

2017 ◽  
Vol 67 ◽  
pp. 228-238 ◽  
Author(s):  
Jonathan de Andrade Silva ◽  
Eduardo Raul Hruschka ◽  
João Gama
Keyword(s):  
Evolutionary Algorithm ◽  
Data Streams ◽  
Variable Number ◽  
Number Of Clusters ◽  
Clustering Data

CLUSTER BASED ENSEMBLE CLASSIFIER GENERATION BY JOINT OPTIMIZATION OF ACCURACY AND DIVERSITY

2013 ◽  
Vol 12 (04) ◽  
pp. 1340003 ◽  
Author(s):  
ASHFAQUR RAHMAN ◽  
BRIJESH VERMA
Keyword(s):  
Evolutionary Algorithm ◽  
Ensemble Classifier ◽  
Variable Number ◽  
Joint Optimization ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Nondominated Set ◽  
Number Of Layers ◽  
Single Objective

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 nonuniform 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 nondominated 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.

Quantitative evaluation of post-bone marrow transplant engraftment status using fluorescent-labeled variable number of tandem repeats

2000 ◽  
Vol 05 (2) ◽  
pp. 129-138
Author(s):  
Robert A. Luhm ◽  
Daniel B. Bellissimo ◽  
Arejas J. Uzgiris ◽  
William R. Drobyski ◽  
Martin J. Hessner
Keyword(s):  
Bone Marrow ◽  
Bone Marrow Transplant ◽  
Quantitative Evaluation ◽  
Tandem Repeats ◽  
Marrow Transplant ◽  
Variable Number
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