S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.

Binary arithmetic optimization algorithm for feature selection

Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm.

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification

In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.

A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

Boosting Archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model's accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

Non-Dominated Sorting Manta Ray Foraging Optimization for Multi-Objective Optimal Power Flow with Wind/Solar/Small- Hydro Energy Sources

This present study describes a novel manta ray foraging optimization approach based non-dominated sorting strategy, namely (NSMRFO), for solving the multi-objective optimization problems (MOPs). The proposed powerful optimizer can efficiently achieve good convergence and distribution in both the search and objective spaces. In the NSMRFO algorithm, the elitist non-dominated sorting mechanism is followed. Afterwards, a crowding distance with a non-dominated ranking method is integrated for the purpose of archiving the Pareto front and improving the optimal solutions coverage. To judge the NSMRFO performances, a bunch of test functions are carried out including classical unconstrained and constrained functions, a recent benchmark suite known as the completions on evolutionary computation 2020 (CEC2020) that contains twenty-four multimodal optimization problems (MMOPs), some engineering design problems, and also the modified real-world issue known as IEEE 30-bus optimal power flow involving the wind/solar/small-hydro power generations. Comparison findings with multimodal multi-objective evolutionary algorithms (MMMOEAs) and other existing multi-objective approaches with respect to performance indicators reveal the NSMRFO ability to balance between the coverage and convergence towards the true Pareto front (PF) and Pareto optimal sets (PSs). Thus, the competing algorithms fail in providing better solutions while the proposed NSMRFO optimizer is able to attain almost all the Pareto optimal solutions.

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. 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 subregions (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 gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on 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.

Gaussian Perturbation Specular Reflection Learning and Golden-Sine-Mechanism-Based Elephant Herding Optimization for Global Optimization Problems

Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.