2 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
Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance
Classification of high-dimensional low sample size (HDLSS) data poses a
challenge in a variety of real-world situations, such as gene expression
studies, cancer research, and medical imaging. This article presents the
development and analysis of some classifiers that are specifically designed for
HDLSS data. These classifiers are free of tuning parameters and are robust, in
the sense that they are devoid of any moment conditions of the underlying data
distributions. It is shown that they yield perfect classification in the HDLSS
asymptotic regime, under some fairly general conditions. The comparative
performance of the proposed classifiers is also investigated. Our theoretical
results are supported by extensive simulation studies and real data analysis,
which demonstrate promising advantages of the proposed classification
techniques over several widely recognized methods.Comment: Accepted to be published at the European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML PKDD), 202