Optimal Feature Selection Algorithm Research Articles

Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm for feature selection

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The Chimp Optimisation Algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm’s performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning Chimp Optimization Algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

Chaotic marine predator optimization algorithm for feature selection in schizophrenia classification using EEG signals

AbstractSchizophrenia is a chronic mental illness that can negatively affect emotions, thoughts, social interaction, motor behavior, attention, and perception. Early diagnosis is still challenging and is based on the disease’s symptoms. However, electroencephalography (EEG) signals yield incredibly detailed information about the activities and functions of the brain. In this study, a hybrid algorithm approach is proposed to improve the search performance of the marine predator algorithm (MPA) based on chaotic maps. For evaluating the performance of the proposed chaotic-based marine predator algorithm (CMPA), benchmark datasets are used. The results of the suggested variation method on the benchmarks show that the Sine Chaotic-based MPA (SCMPA) significantly outperforms the other MPA variants. The algorithm was verified using a public dataset consisting of 14 subjects. Moreover, the proposed SCMPA is essential for EEG electrode selection because it minimizes model complexity and selects the best representative features for providing optimal solutions. The extracted features for each subject were used in the decision tree (DT), random forest (RF), and extra tree (ET) methods. Performance measures showed that the proposed model was successful at differentiating schizophrenia patients (SZ) from healthy controls (HC). In the end, it was demonstrated that the feature selection technique SCMPA, which is the subject of this research, performs significantly better in regard to classification using EEG signals.

An Improved Northern Goshawk Optimization Algorithm for Feature Selection

An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets

Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

Activity recognition plays pivotal role in enhancing functionality for prosthetic devices, ensuring seamless integration with users' movements. However, the complexity arises from the diverse data sources, including acceleration, angular velocity, joint angles, orientation, electromyography (EMG), and marker data, necessitating a robust approach to overcome challenges in information integration. The primary challenge lies in the effective utilization of multiple sensor modalities, each with unique characteristics and potential noise sources. The proposed solution addresses this by employing advanced sensor fusion techniques, such as Kalman filtering, during data collection. Synchronization and resampling ensure temporal consistency, while noise reduction techniques, such as low-pass filters, mitigate signal distortions. To further refine the process, a hybrid optimization-based feature selection Adaptive Step Size in Marine Predators Algorithm (ASSMPA) is introduced, focusing on marker data features. ASSMPA synergizes Marine Predators Algorithm (MPA) and Pathfinder Algorithm (PFA) for optimal feature selection in marine predator pathfinding tasks. The Feature Fusion step integrates attention mechanisms to dynamically weigh the significance of different sensor modalities during the fusion process. This strategic fusion enhances the overall performance of the Multi-Modal Hierarchical Neural Network (MMHNN). The proposed model is implemented using Python.

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

Activity recognition plays pivotal role in enhancing functionality for prosthetic devices, ensuring seamless integration with users' movements. However, the complexity arises from the diverse data sources, including acceleration, angular velocity, joint angles, orientation, electromyography (EMG), and marker data, necessitating a robust approach to overcome challenges in information integration. The primary challenge lies in the effective utilization of multiple sensor modalities, each with unique characteristics and potential noise sources. The proposed solution addresses this by employing advanced sensor fusion techniques, such as Kalman filtering, during data collection. Synchronization and resampling ensure temporal consistency, while noise reduction techniques, such as low-pass filters, mitigate signal distortions. To further refine the process, a hybrid optimization-based feature selection Adaptive Step Size in Marine Predators Algorithm (ASSMPA) is introduced, focusing on marker data features. ASSMPA synergizes Marine Predators Algorithm (MPA) and Pathfinder Algorithm (PFA) for optimal feature selection in marine predator pathfinding tasks. The Feature Fusion step integrates attention mechanisms to dynamically weigh the significance of different sensor modalities during the fusion process. This strategic fusion enhances the overall performance of the Multi-Modal Hierarchical Neural Network (MMHNN). The proposed model is implemented using Python

Abstract 4892: MicroRNA signature and telomere genes in kidney cancer survival

Abstract Introduction: Kidney cancer ranks among the top 10 most prevalent cancers in the United States, clear cell renal cell carcinoma (ccRCC) has the worst disease specific survival histology. MicroRNAs (miRNAs) have emerged as potential prognostic indicators in various cancers, including kidney cancers. Telomerase, a biomarker associated with uncontrolled cancer cell growth, plays a crucial role in the human kidney cortex's telomere shortening over time. However, the link between miRNAs and telomeres in kidney cancer survival remains underexplored. Our study aims to uncover a miRNA signature capable of predicting 5-year survival in ccRCC patients and to investigate the biological relevance of this signature, particularly its shared pathway enrichment mechanisms with telomere genes. Methods: We extracted miRNA expression profiles from The Cancer Genome Atlas database, comprising 166 patients with ccRCC. To identify a robust miRNA signature predicting 5-year survival in ccRCC, we established an Evolutionary Kidney Survival Estimator (EKSE) using an optimal feature selection algorithm and support vector regression. Additionally, we conducted bioinformatics analyses to uncover common biological pathways between miRNAs and telomere genes using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) categories, encompassing biological processes, molecular functions, and cellular components. Results: EKSE identified a robust signature consisting of 37 miRNAs, achieving a mean correlation coefficient and mean absolute error of 0.82±0.01 and 0.65±0.18, respectively between actual and estimated survival times. The top 10 ranked miRNAs in the signature, including hsa-miR-26a-1-3p, hsa-miR-28-5p, hsa-miR-3913-5p, hsa-miR-3170, hsa-miR-148a-5p, hsa-miR-671-3p, hsa-miR-224-3p, hsa-miR-10a-5p, hsa-miR-29b-1-5p, and hsa-miR-106b-5p, also showed promise in diagnosing ccRCC with AUC values ranging from 0.56 to 0.91. Furthermore, bioinformatics analysis revealed that the miRNA signature targeted well known telomere-associated genes such as TERT, DKC1, CTC1, and RTEL1. Notably, miRNA signature and telomere genes shared several common pathways, including cellular senescence (hsa04218) and proteoglycans in cancer (hsa05205). They also had overlapping GO-Molecular functions, such as DNA-binding transcription factor activity (GO:0003700), histone deacetylase binding (GO:0042826), and promoter-specific chromatin binding (GO:1990841). Conclusions: Our findings suggest an association between microRNAs and telomere genes, potentially playing significant roles in the survival of patients with ccRCC. The identified miRNA signature robustly predicts 5-year survival in ccRCC. The shared biological pathways between these miRNAs and telomere genes offer new insights into the mechanisms driving ccRCC, signaling potential for therapeutic targets and improved prognostic markers in kidney cancer. Citation Format: Srinivasulu Yerukala Sathipati, Rohit Sharma, Scott Hebbring. MicroRNA signature and telomere genes in kidney cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4892.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

BinCOA: An Efficient Binary Crayfish Optimization Algorithm for Feature Selection

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.

Horizontal crossover and co-operative hunting-based Whale Optimization Algorithm for feature selection

The Whale Optimization Algorithm (WOA) is a widely-used approach for problem-solving, but it has some inherent limitations such as poor exploration capabilities, susceptibility to local optima, and reduced solution accuracy. To address these drawbacks, this study introduces a novel approach known as Horizontal Crossover and Co-operative-hunting-based WOA (HCCWOA). This enhanced algorithm incorporates a weight, co-operative learning techniques, and a horizontal crossover strategy into the WOA framework. The introduction of horizontal crossover bolsters the exploration capabilities of WOA, while the integration of co-operative learning techniques and an inertia weight enhances its exploitation abilities. Recognizing the significance of feature selection in this context, the proposed algorithm is applied in a wrapper mode with the K-Nearest Neighbor (KNN) classifier to select relevant features. The effectiveness of the HCCWOA is rigorously evaluated on twelve classical datasets sourced from the UCI repository. A comprehensive comparison is conducted with eight well-established metaheuristic algorithms and five recent variations of WOA. Performance metrics, including maximum accuracy, minimum fitness, and minimum feature count, are considered in this comparative analysis. The simulation results affirm that the HCCWOA outperforms other algorithms in at least six out of the twelve datasets. This enhanced performance is further substantiated through statistical analyses, including Friedman's rank test, paired-sample Wilcoxon signed-rank test, two-way ANOVA test, T-test, and boxplot analysis. The combination of empirical results and statistical validation supports the superior effectiveness of the proposed HCCWOA approach, highlighting its ability to effectively explore feature spaces and select the most relevant characteristics for classification tasks.

Intrusion detection for Industrial Internet of Things based on deep learning

Intrusion detection technology can actively detect abnormal behaviors in the network and is important to the security of Industrial Internet of Things (IIOT). However, there are some issues with the current intrusion detection technology for IIOT, such as extreme imbalance in the number of samples of different classes in the dataset, redundant and meaningless features in the samples, and the inability of traditional intrusion detection methods to meet the requirements of detection accuracy in the increasingly complex IIOT. In view of the extreme imbalance of classes, this paper applies the hierarchical clustering algorithm to the under-sampling technology, which reduces the number of majority samples while reducing the loss of information of majority samples, and solves the problem of missing detection and false detection of minority samples caused by sample imbalance. In order to avoid feature redundancy and interference, this paper proposes an optimal feature selection algorithm based on greedy thought. This algorithm can obtain the optimal feature subset of each type of data in the data set, thus eliminating redundant and interfering features. Aiming at the problem of insufficient detection ability of traditional detection methods, this paper proposes a deep neural network intrusion detection model based on the parallel connection of global and local subnetworks. This model obtains the overall benchmark detection of the dataset through the deep neural network, and then strengthens the detection effect of each subclass through the parallel connection of subnetworks, greatly improving the performance of the intrusion detection algorithm. The experimental results show that the method described in this paper can improve the intrusion detection for IIOT.

A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data

A comparative study of optimization algorithms for feature selection on ML-based classification of agricultural data

A Meta-Heuristic Algorithm-Based Feature Selection Approach to Improve Prediction Success for Salmonella Occurrence in Agricultural Waters

The presence of Salmonella in agricultural waters may be a source of produce contamination. Recently, the performance of various algorithms has been tested for the prediction of indicator bacteria population and pathogen occurrence in agricultural water sources. The purpose of this study was to evaluate the effect of meta-heuristic optimization algorithms for feature selection to increase the Salmonella occurrence prediction performance of commonly used algorithms in agricultural waters. Previously collected dataset from six agricultural ponds in Central Florida included the population of indicator microorganisms, physicochemical water attributes, and weather station measurements. Salmonella presence was also reported with PCR-confirmed method in data set. Features were selected by binary meta-heuristic optimization methods including differential evolution optimization (DEO), grey wolf optimization (GWO), Harris hawks optimization (HHO) and particle swarm optimization (PSO). Each meta-heuristic method was run for 100 times for the extraction of features before classification analysis. Selected features after optimization were used in the K-nearest neighbor algorithm (kNN), support vector machine (SVM) and decision tree (DT) classification methods. Microbiological indicators were ranked as the first or second features by all optimization algorithms. Generic Escherichia coli was selected as the first feature 81 and 91 times out of 100 by GWO and DEO, respectively. The meta-heuristic optimization algorithms for the feature selection process followed by machine learning classification methods yielded the prediction accuracy between 93.57 and 95.55%. Meta-heuristic optimization algorithms had a positive effect to improve Salmonella prediction success in agricultural waters despite spatio-temporal variations in agricultural water quality.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ± 0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model

BackgroundExtranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.MethodsThere were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets.ResultsThe EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE.ConclusionsThe EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.

A Modified Binary Arithmetic Optimization Algorithm for Feature Selection

Feature selection chooses the optimal subset from the feature set without scarifying the information carried by the dataset. It is considered a complex combinatorial problem, so classical optimization techniques fail to solve it when the feature set becomes larger. Meta-heuristic approaches are well known to solve complex optimization problems; hence these algorithms have been successfully applied to extract optimal feature subsets. The arithmetic Optimization Algorithm is a newly proposed mathematics-based meta-heuristic search algorithm successfully applied to solve optimization problems. However, it has been observed that AOA experiences a poor exploration phase. Hence in the present work, a Modified Binary Arithmetic Optimization Algorithm (MB-AOA) is proposed, which solves the poor exploration problem of standard AOA. In the MB-AOA, instead of utilizing a single best solution, an optimal solution set that gradually shrinks after each successive iteration is applied for better exploration during initial iterations. Also, instead of a fixed search parameter (μ), the MB-AOA utilizes a variable parameter suitable for binary optimization problems. The proposed method is evaluated over seven real-life datasets from the UCI repository as a feature selection wrapper method and compared with standard AOA over two performance metrics, Average Accuracy, F-score, and the generated feature subset size. MB-AOA has performed better in six datasets regarding F-score and average accuracy. The obtained results from the simulation process demonstrate that the MB-AOA can select the relevant features, thus improving the classification task’s overall accuracy levels.