Abstract 3 Meaningful collaboration among heterogeneous stakeholders is essential for
achieving sustainability in agri-food supply chains. A substantial amount of research has …

Feature selection (FS) is a machine learning process commonly used to reduce the high
dimensionality problems of datasets. This task permits to extract the most representative …

Feature selection has been widely used in data mining and machine learning tasks to make
a model with a small number of features which improves the classifier's accuracy. In this …

The rapid growth of World Wide Web has led to explosive growth of information. As most of
information is stored in the form of texts, text mining has gained paramount importance. With …

Feature selection algorithm explores the data to eliminate noisy, irrelevant, redundant data,
and simultaneously optimize the classification performance. In this paper, a classification …

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 …

Rough set theory is one of the effective methods to feature selection, which can preserve the
meaning of the features. The essence of rough set approach to feature selection is to find a …

Feature selection as a combinatorial optimization problem is an important preprocessing
step in data mining; which improves the performance of the learning algorithms with the help …