@inproceedings{ccf80b4d200242b78586e8c78c5bf424,
title = "Elastic Multi-stage Decision Rules for Infrequent Class",
abstract = "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.",
keywords = "association mining, decision tree, infrequent classes, large datasets, multi-stage rule generation, rare classes, recursive partition, rule sets",
author = "Soma Datta and Susan Mengel",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; null ; Conference date: 23-11-2016 Through 25-11-2016",
year = "2017",
month = oct,
day = "2",
doi = "10.1109/ISCMI.2016.20",
language = "English",
series = "Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "110--114",
booktitle = "Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016",
}