Soft computing (SC) is a field of computer science that resembles the processes of the
human brain. While conventional hard computing is run based on crisp values and binary …

We propose an embedded/integrated feature selection method based on neural networks
with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on …

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called
as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a …

In this paper, we propose a new hybrid ant colony optimization (ACO) algorithm for feature
selection (FS), called ACOFS, using a neural network. A key aspect of this algorithm is the …

This paper presents a new feature selection (FS) algorithm based on the wrapper approach
using neural networks (NNs). The vital aspect of this algorithm is the automatic …

This paper presents an online feature selection algorithm using genetic programming (GP).
The proposed GP methodology simultaneously selects a good subset of features and …

The paper presents a novel set of features based on surroundedness property of a signature
(image in binary form) for off-line signature verification. The proposed feature set describes …

We present an integrated algorithm for simultaneous feature selection (FS) and designing of
diverse classifiers using a steady state multiobjective genetic programming (GP), which …

Most methods of classification either ignore feature analysis or do it in a separate phase,
offline prior to the main classification task. This paper proposes a neuro-fuzzy scheme for …

In recent past the need for ubiquitous people identification has increased with the
proliferation of humanrobot interaction systems. In this paper we propose a methodology of …