NDLI: Mining data streams with concept drifts using genetic algorithm

Content Provider	Springer Nature Link
Author	Vivekanandan, Periasamy Nedunchezhian, Raju
Copyright Year	2011
Abstract	Recent research shows that rule based models perform well while classifying large data sets such as data streams with concept drifts. A genetic algorithm is a strong rule based classification algorithm which is used only for mining static small data sets. If the genetic algorithm can be made scalable and adaptable by reducing its I/O intensity, it will become an efficient and effective tool for mining large data sets like data streams. In this paper a scalable and adaptable online genetic algorithm is proposed to mine classification rules for the data streams with concept drifts. Since the data streams are generated continuously in a rapid rate, the proposed method does not use a fixed static data set for fitness calculation. Instead, it extracts a small snapshot of the training example from the current part of data stream whenever data is required for the fitness calculation. The proposed method also builds rules for all the classes separately in a parallel independent iterative manner. This makes the proposed method scalable to the data streams and also adaptable to the concept drifts that occur in the data stream in a fast and more natural way without storing the whole stream or a part of the stream in a compressed form as done by the other rule based algorithms. The results of the proposed method are comparable with the other standard methods which are used for mining the data streams.
Starting Page	163
Ending Page	178
Page Count	16
File Format	PDF
ISSN	02692821
Journal	Artificial Intelligence Review
Volume Number	36
Issue Number	3
e-ISSN	15737462
Language	English
Publisher	Springer Netherlands
Publisher Date	2011-02-17
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Classification Online learning Genetic algorithm (GA) Concept drift Artificial Intelligence (incl. Robotics) Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Linguistics and Language

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