Masters Theses
Abstract
"The primary aim of this research is to develop an intelligent system for online data mining for classification problems. A smart system is developed by employing techniques such as neural networks and genetic algorithms. Evolving neural network architecture previously developed in the Smart Engineering Systems Laboratory at the University of Missouri--Rolla is used for developing this system. The architecture uses genetic algorithms for automatic generation of neural network, feature selection and aggregating the neural networks. Genetic algorithms are also used to optimize the neural network weights. Ensembles of classifiers are used to have diverse networks to handle noise and improve the performance of the system. The proposed system can be used as a real-time system, which typically learns from the existing data. As new information is available over a period of time, the system learns new information in addition to the previous information, thus tuning itself depending on how the system is used. The system was tested on various data sets like heart, liver-disorder and vehicle, and produced better results than traditional neural networks and evolving neural networks in batch training in which all the training data is presented at the same time. The system can be used for both offline and online training. A GUI is developed for ease of application. The main purpose of this research is to develop a smart real-time system that can adapt to a non-stationary environment on its own ability. The system can be used in many applications where user interaction always exists, like market basket analysis, intelligent user interface, web and intranet management, network management in telecommunications and process control in manufacturing"--Abstract, page iii.
Advisor(s)
Dagli, Cihan H., 1949-
Committee Member(s)
Enke, David Lee, 1965-
St. Clair, Daniel C.
Department(s)
Engineering Management and Systems Engineering
Degree Name
M.S. in Engineering Management
Publisher
University of Missouri--Rolla
Publication Date
Spring 2004
Pagination
viii, 55 pages
Note about bibliography
Includes bibliographical references (pages 53-54).
Rights
© 2004 Nadeem Ahmed, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Subject Headings
Data miningNeural networks (Computer science)
Thesis Number
T 8487
Print OCLC #
56479046
Recommended Citation
Ahmed, Nadeem, "Online data mining using evolving neural networks" (2004). Masters Theses. 2499.
https://scholarsmine.mst.edu/masters_theses/2499
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