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Soft Computing

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18.02.2019 | Methodologies and Application

A hybrid approach using rough set theory and hypergraph for feature selection on high-dimensional medical datasets

verfasst von: M. R. Gauthama Raman, Somu Nivethitha, Krithivasan Kannan, V. S. Shankar Sriram

Erschienen in: Soft Computing | Ausgabe 23/2019

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Abstract

‘Curse of Dimensionality’—massive generation of high-dimensional medical datasets from various biomedical applications hardens the data analytic process for precise medical diagnosis. The design of an efficient feature selection technique for finding the optimal feature subset can be devised as a prominent solution to the above-said challenge. Further, it also improves the accuracy and minimizes the computational complexity of the learning model. The state-of-the-art feature selection techniques based on heuristic and statistical functions suffer from significant challenges in terms of classification accuracy, time complexity, etc. Hence, this paper presents Rough Set Theory and Hypergraph (RSHGT)-based feature selection technique to identify the optimal feature subset for accurate medical diagnosis. Experimental validations using six medical datasets from the Kent Ridge Biomedical dataset repository prove the efficiency of RSHGT in terms of reduct size, accuracy, precision, recall, and time complexity.

Vorheriger Artikel A review on the application of fuzzy transform in data and image compression

Nächster Artikel Extracting and reusing blocks of knowledge in learning classifier systems for text classification: a lifelong machine learning approach

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Titel: A hybrid approach using rough set theory and hypergraph for feature selection on high-dimensional medical datasets
verfasst von: M. R. Gauthama Raman
Somu Nivethitha
Krithivasan Kannan
V. S. Shankar Sriram
Publikationsdatum: 18.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 23/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-03818-6

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