Efficient and effective data classification is one of the most fundamental problems of computer science due to a huge number of important applications. As such, there have been many models that are capable of doing some form of data classification using a large number of varied algorithms and architectures. However, few share properties that implicitly take advantage of the distributed environment where classification has its most potential.
Therefore, to resolve this dilemma in this thesis a generic classification architecture com posed of mutually adaptive distributed heterogeneous agents was designed, implemented and experimentally analyzed. The system defined creates a multi-agent distributed ar chitecture whereby individual agents cooperate and collaborate with each other to autonomously perform classification on arbitrary data using a hierarchical paradigm.
The main contributions of this work are the following: agents follow hierarchical pro cesses to form an organization which can autonomously train, test, assign and evaluate work of other agents. Through experimental analysis it was found that this design improves the classification accuracy relative to previously implemented classification algorithms and architectures. Furthermore this analysis shows that the generated architecture will au tonomously adapt to previously unlearned data and responds to failures with little or no degradation of classification quality.
Library of Congress Subject Headings
Intelligent agents (Computer software); Discriminant analysis; Automatic classification; Computer algorithms; Data mining
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Harlow, Joshua Alan, "Mutually Adaptive Distributed Heterogeneous Agents for Data Classification" (2007). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus