scholarly journals An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

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.

scholarly journals Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2321
Author(s):  
Ahmed A. Ewees ◽  
Mohammed A. A. Al-qaness ◽  
Laith Abualigah ◽  
Diego Oliva ◽  
Zakariya Yahya Algamal ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Search Strategies ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

scholarly journals Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection

2020 ◽  
Vol 5 (6) ◽  
pp. 1420-1428
Author(s):  
Omar S. Qasim ◽  
Mohammed Sabah Mahmoud ◽  
Fatima Mahmood Hasan
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Feature Selection Method ◽  
Statistical Dependence ◽  
K Nearest Neighbors ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Dragonfly Algorithm ◽  
The Cost

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.

scholarly journals Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

2019 ◽  
Vol 1 (5) ◽  
Author(s):  
Maryam Shuaib ◽  
Shafi’i Muhammad Abdulhamid ◽  
Olawale Surajudeen Adebayo ◽  
Oluwafemi Osho ◽  
Ismaila Idris ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Whale Optimization Algorithm ◽  
Rotation Forest ◽  
Whale Optimization ◽  
Email Spam

Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

2017 ◽  
Vol 7 (4) ◽  
pp. 51-62 ◽  
Author(s):  
Noria Bidi ◽  
Zakaria Elberrichi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Support Vector ◽  
Feature Subset ◽  
Biomedical Data ◽  
Features Selection ◽  
Cancer Data ◽  
Search Optimization ◽  
Fitness Value

Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets.

Sign in / Sign up

Export Citation Format

Share Document