NDLI: Hypergraph Based Feature Selection Technique for Medical Diagnosis

Content Provider	Springer Nature Link
Author	Somu, Nivethitha Raman, M. R. Gauthama Kirthivasan, Kannan Sriram, V. S. Shankar
Copyright Year	2016
Abstract	The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K – Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K – Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.
Starting Page	1
Ending Page	16
Page Count	16
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	40
Issue Number	11
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2016-09-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rough set theory (RST) Hypergraph K – Helly property High dimensional datasets Feature selection Medical diagnosis Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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