Delen, "Computer-aided diagnosis of Parkinson's disease using complex-valued neural networks and mRMR
feature selection algorithm," Journal of Healthcare Engineering, vol.
With training data, mRMR
was first used to find the candidate features (15 features), and then, GAFS with NB, SVM, and NN was applied to find the most valid features and classifiers.
Although the mRMR
partly solves the first problem in MIFS and MIFS-U, the performance is still comparable with those of MIFS and MIFS-U .
algorithm, both the correlation between features and feature-class were concerned to get the best feature set .
Volleyball: Hazem x Za'abeel Strom 3-2 (25-19, 21-25, 25-20, 20-25, 15-10); MRMR
x Spider 3-2 (25-16, 25-23, 13-25, 17-25, 15-12).
The CFS, mRMR
, ReliefF, and SVM algorithms are implemented using Weka 3.6.
In the following content, the mathematical description of relevance, redundancy, and complementarity is interpreted through the introduction of MRMR
To improve the accuracy of the classifier, features are extracted using fast Fourier transform (FFT) and reduction of features is performed using Minimal-Redundancy-Maximal-Relevance (MRMR
First, instead of extracting features from a handcrafted domain, a two-stage feature selection method combining ReliefF and mRMR
algorithms was developed to select optimal yet compact feature subsets, which takes both attributes weights and redundancy reduction into account.
The minimum redundancy maximum relevance (mRMR
) feature selection method is a feature selection method for finding a set of features that have the highest relevance with the target class and are also maximally dissimilar to each other based on the mutual information theory.
Graph-based MObPSO  was modelled through kernel P system for the following reasons: (1) it has ability to model genes (nodes) and define relationships between them (edges); (2) it has a higher accuracy as compared with flat (filter and wrapper) methods, sequential backward elimination (SBE), correlation-based feature selection (CFS), minimum redundancy maximum relevance (mRMR
), and sequential forward search (SFS).
Some of rock mass classifications such as Q, RMR and MRMR
systems have been applied successfully in tunneling and underground excavations.