Feature selection for face recognition based on multi-objective evolutionary wrappers

Vignolo, Leandro D.; Milone, Diego H.; Scharcanski, Jacob

doi:10.1016/j.eswa.2013.03.032

Cited by 83 publications

(36 citation statements)

References 38 publications

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“…They need to train a predictor (classifier or regression model) to evaluate each feature subset; therefore, they are more precise but slower than the filters. The main challenges in wrapper methods are selecting a proper predictor (Li et al 2009;Maldonado and Weber 2009;Monirul Kabir, Monirul Islam, and Murase 2010;Sánchez-Maroño and Alonso-Betanzos 2011) and how to generate appropriate subsets (Macas et al 2012;Tay and Cao 2001;Vignolo, Milone, and Scharcanski 2013). Finally, hybrid methods use both filter and wrapper evaluators simultaneously (Bermejo et al 2012;Bermejo, Gámez, and Puerta 2011;Gheyas and Smith 2010;Ruiz et al 2012).…”

Section: Feature Selectionmentioning

confidence: 99%

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Moshki

Kabiri

Mohebalhojeh

2015

Applied Artificial Intelligence

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Feature selection in high-dimensional data is one of the active areas of research in pattern recognition. Most of the algorithms in this area try to select a subset of features in a way to maximize the accuracy of classification regardless of the number of selected features that affect classification time. In this article, a new method for feature selection algorithm in high-dimensional data is proposed that can control the trade-off between accuracy and classification time. This method is based on a greedy metaheuristic algorithm called greedy randomized adaptive search procedure (GRASP). It uses an extended version of a simulated annealing (SA) algorithm for local search.In this version of SA, new parameters are embedded that allow the algorithm to control the trade-off between accuracy and classification time. Experimental results show supremacy of the proposed method over previous versions of GRASP for feature selection. Also, they show how the trade-off between accuracy and classification time is controllable by the parameters introduced in the proposed method.

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Section: Feature Selectionmentioning

confidence: 99%

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Moshki

Kabiri

Mohebalhojeh

2015

Applied Artificial Intelligence

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“…The goal of the feature selection methods is to seek the relevant features with the most predictive information from the original feature set. Feature selection has been established as an important technique in many practical applications such as text processing (Aghdam et al, 2009;Shamsinejadbabki and Saraee, 2011;Uğuz, 2011), face recognition (Chakraborti and Chatterjee, 2014;Kanan and Faez, 2008;Vignolo et al, 2013), image retrieval (da Silva et al, 2011;Rashedi et al, 2013), medical diagnosis (Inbarani et al, 2014), case-base reasoning (Zhu et al, 2015), collaborative filtering basd recommender systems (Ramezani et al, 2013) and bioinformatics (Jaganathan and Kuppuchamy, 2013).…”

Section: Introductionmentioning

confidence: 99%

A graph theoretic approach for unsupervised feature selection

Moradi

Rostami

2015

Engineering Applications of Artificial Intelligence

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“…The experiments were performed using University of ESSEX face Recognition Data. In another work, Vignolo et al (2013) also used ASM for extracting features, but with a modi ed version of GA called Multi-Objective Genetic Algorithm (MOGA), in which the rank of an individual is the number of chromosomes in the population by which it dominates. Also, the author proposed an aggregative tness function, which combines classi cation accuracy and the number of features in a single equation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

Plichoski

Chidambaram

Parpinelli

2018

RBCA

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A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.Key words: Bio-inspired algorithms; Evolutionary algorithms; Face Recognition; Natural Computing; Optimization; Swarm algorithms ResumoUma ampla gama de abordagens para sistemas de reconhecimento facial (FR) 2D pode ser encontrada na literatura devido a sua alta aplicabilidade e também por questões que necessitam de mais investigação, incluindo oclusão, variações de escala, expressão facial e iluminação. Nos últimos anos, um número crescente de sistemas de FR 2D usando algoritmos de Inteligência de Enxame e Computação Evolucionária surgiram. O presente trabalho traz uma Revisão Sistemática de Literatura (SLR) atualizada sobre o uso da Inteligência de Enxame e Computação Evolucionária aplicada em sistemas de FR 2D. Além disso, esta revisão analisa e aponta as principais técnicas e algoritmos utilizados e sugere alguns direcionamentos para pesquisas futuras.

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Feature selection for face recognition based on multi-objective evolutionary wrappers

Cited by 83 publications

References 38 publications

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Scalable Feature Selection in High-Dimensional Data Based on GRASP

A graph theoretic approach for unsupervised feature selection

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

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