Prediction of cancer using customised fuzzy rough machine learning approaches

Prediction of cancer using customised fuzzy rough machine learning approaches

This Letter proposes a customised approach for attribute selection applied to the fuzzy rough quick reduct algorithm. The unbalanced data is balanced using synthetic minority oversampling technique. The huge dimensionality of the cancer data is reduced using a correlation-based filter. The dimension...

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Bibliographic Details
Main Authors: Chinnaswamy Arunkumar, Srinivasan Ramakrishnan
Format: Article
Language:English
Published: Wiley 2018-10-01
Series:Healthcare Technology Letters
Subjects:
lung
fuzzy set theory
cancer
rough set theory
pattern classification
learning (artificial intelligence)
sampling methods
approximation theory
medical computing
cancer data
fuzzy rough quick reduct algorithm
Lukasiewicz fuzzy implicator
fuzzy approximation
fuzzy rough machine learning approaches
fuzzy triangular norm operator
correlation-based filter
leukaemia
central nervous system
ovarian datasets
McNemar test
chi-squared test
Matthew's correlation coefficient
Online Access:https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5055

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https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5055

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