Paper Info
Title
A Novel Simulated Annealing Based Training Algorithm for Data Stream Processing Ensemble Classifier.
Abstract
Training of compound ensemble classifier systems might be computationally complex and hence time consuming task. Not only elementary classifiers are to be trained, but also model of the ensemble has to be updated. Therefore, an efficiency of the training shall be considered as a compound quality which consists of not only a classification accuracy but also a running time. This gains a special importance while dealing with data streams where data arrive at high pace and the system update shall be done promptly. In this paper we present an application of Simulated Annealing based algorithm for training of data stream processing ensemble. The evaluation of our method is performed in series of experiments which show that our ensemble perform very effectively in term of accuracy and processing time.
Year
DOI
Venue
2017
10.1007/978-3-319-59162-9_46
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2017
Field
DocType
Volume
Simulated annealing,Data mining,Data stream mining,Data stream processing,Pattern recognition,Computer science,Algorithm,Adaptive simulated annealing,Artificial intelligence,Classifier (linguistics)
Conference
578
ISSN
Citations 
PageRank 
2194-5357
0
0.34
References 
Authors
5
1
Name
Order
Citations
PageRank
Konrad Jackowski113610.46