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A cluster based hybrid feature selection approach
(Universidade Federal do Rio Grande do Norte – UFRNSociedade Brasileira de Computação – SBCNatal, 2015-11)
Data collection and storage capacities have increased significantly in the past decades. In order to cope with the increasingly complexity of data, feature selection methods have become an omnipresent preprocessing step ...
BBA: A binary bat algorithm for feature selection
(2012-12-01)
Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a ...
Label construction for multi-label feature selection
(Universidade de São Paulo - USPUniversidade Federal de São Carlos - UFSCarCentro de Robótica de São Carlos - CROBSociedade Brasileira de Computação - SBCSociedade Brasileira de Automática - SBASão Carlos, 2014-10)
Multi-label learning handles datasets where each instance is associated with multiple labels, which are often correlated. As other machine learning tasks, multi-label learning also suffers from the curse of dimensionality, ...
Feature selection through gravitational search algorithm
(IEEE, 2011-01-01)
In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the ...
Feature Selection Using Geometric Semantic Genetic Programming
(Assoc Computing Machinery, 2017-01-01)
Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been ...
Machine Learning for Web Intrusion Detection: A Comparative Analysis of Feature Selection Methods mRMR and PFI
(2020-01-01)
Select from the best features in a complex dataset that is a critical task for machine learning algorithms. This work presents a comparative analysis between two resource selection techniques: Minimum Redundancy Maximum ...
On the use of support mechanisms to perform experimental variables selection
(2020-01-01)
The selection of variables in a given experiment is crucial, since it is the theoretical foundation that guides how data should be collected and analyzed. However, selecting variables is an intricate activity, especially ...
A Binary Krill Herd Approach for Feature Selection
(Ieee Computer Soc, 2014-01-01)
Meta-heuristic-based feature selection has been paramount in the last years, mainly because of its simplicity, effectiveness and also efficiency in some cases. Such approaches are based on the social dynamics of living ...
From explanations to feature selection: assessing SHAP values as feature selection mechanism
(Ieee, 2020-01-01)
Explainability has become one of the most discussed topics in machine learning research in recent years, and although a lot of methodologies that try to provide explanations to black-box models have been proposed to address ...
Artificial Immune Systems with Negative Selection Applied to Health Monitoring of Aeronautical Structures
(Trans Tech Publications Ltd, 2014-01-01)
In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative selection. Inspired by a biological process, the principle of discrimination proper/non-proper, ...