TY - GEN
T1 - Optimized fuzzy decision tree data mining for engineering applications
AU - Evans, Liam
AU - Lohse, Niels
PY - 2011
Y1 - 2011
N2 - Manufacturing organizations are striving to remain competitive in an era of increased competition and every-changing conditions. Manufacturing technology selection is a key factor in the growth of an organization and a fundamental challenge is effectively managing the computation of data to support future decision-making. Classification is a data mining technique used to predict group membership for data instances. Popular methods include decision trees and neural networks. This paper investigates a unique fuzzy reasoning method suited to engineering applications using fuzzy decision trees. The paper focuses on the inference stages of fuzzy decision trees to support decision-engineering tasks. The relaxation of crisp decision tree boundaries through fuzzy principles increases the importance of the degree of confidence exhibited by the inference mechanism. Industrial philosophies have a strong influence on decision practices and such strategic views must be considered. The paper is organized as follows: introduction to the research area, literature review, proposed inference mechanism and numerical example. The research is concluded and future work discussed.
AB - Manufacturing organizations are striving to remain competitive in an era of increased competition and every-changing conditions. Manufacturing technology selection is a key factor in the growth of an organization and a fundamental challenge is effectively managing the computation of data to support future decision-making. Classification is a data mining technique used to predict group membership for data instances. Popular methods include decision trees and neural networks. This paper investigates a unique fuzzy reasoning method suited to engineering applications using fuzzy decision trees. The paper focuses on the inference stages of fuzzy decision trees to support decision-engineering tasks. The relaxation of crisp decision tree boundaries through fuzzy principles increases the importance of the degree of confidence exhibited by the inference mechanism. Industrial philosophies have a strong influence on decision practices and such strategic views must be considered. The paper is organized as follows: introduction to the research area, literature review, proposed inference mechanism and numerical example. The research is concluded and future work discussed.
KW - Classification and Prediction
KW - Fuzzy Decision Tree (FDT)
KW - Intelligent Decision- Making
KW - Knowledge Management
KW - Manufacturing Technology Selection
UR - http://www.scopus.com/inward/record.url?scp=80052226769&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23184-1_18
DO - 10.1007/978-3-642-23184-1_18
M3 - Conference contribution
AN - SCOPUS:80052226769
SN - 9783642231834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 239
BT - Advances in Data Mining
T2 - 11th Industrial Conference on Data Mining, ICDM 2011
Y2 - 30 August 2011 through 3 September 2011
ER -