A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

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An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

10.21203/rs.3.rs-636151/v1 ◽

2021 ◽

Author(s):

Imène NEGGAZ ◽

Hadria FIZAZI

Keyword(s):

Computer Vision ◽

Feature Selection ◽

Optimization Algorithm ◽

Social Life ◽

Feature Selection Method ◽

Human Person ◽

Emperor Penguin ◽

Gender Recognition ◽

Facial Analysis ◽

Face Area

Abstract Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to the technological progress and the increase of mobile applications. HFA explores several issues as gender recognition, facial expression, age, and race recognition for automatically understanding social life. In addition, the development of several algorithms inspired by swarm intelligence, biological inspiration, and physical/mathematical rules allow giving another dimension of feature selection in the field of machine learning and computer vision. This paper develops a novel wrapper feature selection method for gender recognition using the Archimedes optimization algorithm (AOA). The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several sub-regions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram oriented gradient (HOG), or Grey level co-occurrence matrix (GLCM). The proposed method (AOA) is assessed on two publicly datasets: Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The experimental results show a good performance of AOA compared to other recent and competitive optimizers as Sine cosine algorithm (SCA), Henry Gas Solubility Optimization (HGSO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Harris Hawks Optimize (HHO), Multi-verse Optimizer (MVO) and Manta-ray Foraging Optimizer (MRFO) in terms of accuracy and the number of the selected area.

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A method for assessing the uncertainty in feature selection tasks

10.1117/12.657935 ◽

2006 ◽

Author(s):

Yulei Jiang

Keyword(s):

Feature Selection ◽

Selection Tasks

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Employing discrete Bayes error rate for discretization and feature selection tasks

2002 IEEE International Conference on Data Mining, 2002. Proceedings. ◽

10.1109/icdm.2002.1183916 ◽

2003 ◽

Cited By ~ 1

Author(s):

A. Mittal ◽

Loong-Fah Cheong

Keyword(s):

Feature Selection ◽

Error Rate ◽

Bayes Error Rate ◽

Bayes Error ◽

Selection Tasks

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Data Mining Exploration System for Feature Selection Tasks

2006 International Conference on Hybrid Information Technology ◽

10.1109/ichit.2006.253500 ◽

2006 ◽

Cited By ~ 2

Author(s):

Z. Suraj ◽

Delimata

Keyword(s):

Data Mining ◽

Feature Selection ◽

Selection Tasks ◽

Mining Exploration

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Hybrid Group Search Optimizer for Plant Leaf Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2486.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 3557-3563

Keyword(s):

Feature Selection ◽

The Other ◽

Unstructured Data ◽

Broad Application ◽

Plant Leaf ◽

Nature Inspired Algorithm ◽

Selection Tasks ◽

Group Search ◽

Hybrid Group

The analysis of Big Data is a data mining discipline in which large quantity of unstructured data is analysed which can be challenging to store and also to retrieve efficiently. The classification of plants is based on the identification of leaf that has a very broad application in both agriculture and medicine. In this work, a method which is computerized is used to recognize a plant leaf based on images is proposed. The proposed method extracts features from the image and these are used for classifying the plant leaf. The process of deciding on the subset for all relevant features to be used in the construction of a system is known as feature selection. The Group Search Optimizer (GSO) is a nature-inspired algorithm that possesses all the qualities used effectively to solve feature selection tasks. In this work, there is a GSO-based algorithm of feature selection along with fuzzy logic and the classifier of a Neural Network (NN) is proposed. The results of the experiment prove the proposed method (GSO-NN) was able to achieve a better level of performance compared to the other methods.

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