3 research outputs found
Group-Feature (Sensor) Selection With Controlled Redundancy Using Neural Networks
In this paper, we present a novel embedded feature selection method based on
a Multi-layer Perceptron (MLP) network and generalize it for group-feature or
sensor selection problems, which can control the level of redundancy among the
selected features or groups. Additionally, we have generalized the group lasso
penalty for feature selection to encompass a mechanism for selecting valuable
group features while simultaneously maintaining a control over redundancy. We
establish the monotonicity and convergence of the proposed algorithm, with a
smoothed version of the penalty terms, under suitable assumptions. Experimental
results on several benchmark datasets demonstrate the promising performance of
the proposed methodology for both feature selection and group feature selection
over some state-of-the-art methods
Understanding the classes better with class-specific and rule-specific feature selection, and redundancy control in a fuzzy rule based framework
Recently, several studies have claimed that using class-specific feature
subsets provides certain advantages over using a single feature subset for
representing the data for a classification problem. Unlike traditional feature
selection methods, the class-specific feature selection methods select an
optimal feature subset for each class. Typically class-specific feature
selection (CSFS) methods use one-versus-all split of the data set that leads to
issues such as class imbalance, decision aggregation, and high computational
overhead. We propose a class-specific feature selection method embedded in a
fuzzy rule-based classifier, which is free from the drawbacks associated with
most existing class-specific methods. Additionally, our method can be adapted
to control the level of redundancy in the class-specific feature subsets by
adding a suitable regularizer to the learning objective. Our method results in
class-specific rules involving class-specific subsets. We also propose an
extension where different rules of a particular class are defined by different
feature subsets to model different substructures within the class. The
effectiveness of the proposed method has been validated through experiments on
three synthetic data sets