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Feature selection simultaneously preserving both class and cluster structures
When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance.
Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework
Unlike traditional feature selection methods, the class-specific feature selection methods select an optimal feature subset for each class.
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach
It considers the following important issues relevant to dimensionality reduction-based data visualization: (i) preservation of neighborhood relationships, (ii) handling data on a non-linear manifold, (iii) the capability of predicting projections for new test data points, (iv) interpretability of the system, and (v) the ability to reject test points if required.
