TY - GEN
T1 - Elastic Multi-stage Decision Rules for Infrequent Class
AU - Datta, Soma
AU - Mengel, Susan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - Typically, decision trees are used to represent knowledge by rule generation. To have a better understanding of the rules, it is sometimes necessary to minimize the number of nodes by minimizing the depth of the tree. This study optimizes the depth of the tree by minimizing the number of nodes. Rules that are generated using either decision trees or class association mining are from the major class of the dataset. To enable rules to be created for the infrequent class, this study uses an elastic method, Elastic Multi-Stage Decision Methodology (EMSDM), to create rules for the infrequent group. EMSDM is elastic in that it expands and contracts to accommodate the characteristics of the dataset. In addition, the data analysis occurs in stages: clustering, minimizing the depth of the decision tree, and association mining, to increase the ability of EMSDM to find infrequent class rules. EMSDM shows promise to find infrequent class rules with increased accuracy.
AB - Typically, decision trees are used to represent knowledge by rule generation. To have a better understanding of the rules, it is sometimes necessary to minimize the number of nodes by minimizing the depth of the tree. This study optimizes the depth of the tree by minimizing the number of nodes. Rules that are generated using either decision trees or class association mining are from the major class of the dataset. To enable rules to be created for the infrequent class, this study uses an elastic method, Elastic Multi-Stage Decision Methodology (EMSDM), to create rules for the infrequent group. EMSDM is elastic in that it expands and contracts to accommodate the characteristics of the dataset. In addition, the data analysis occurs in stages: clustering, minimizing the depth of the decision tree, and association mining, to increase the ability of EMSDM to find infrequent class rules. EMSDM shows promise to find infrequent class rules with increased accuracy.
KW - association mining
KW - decision tree
KW - infrequent classes
KW - large datasets
KW - multi-stage rule generation
KW - rare classes
KW - recursive partition
KW - rule sets
UR - http://www.scopus.com/inward/record.url?scp=85034622921&partnerID=8YFLogxK
U2 - 10.1109/ISCMI.2016.20
DO - 10.1109/ISCMI.2016.20
M3 - Conference contribution
AN - SCOPUS:85034622921
T3 - Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
SP - 110
EP - 114
BT - Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
Y2 - 23 November 2016 through 25 November 2016
ER -