For Feature Selection Research Articles

Sine Cosine Algorithm Based on Optimal Convolutional Autoencoder for Intrusion Detection and Classification Models

Network security comprises a multifaceted method that aims to protect computer networks from malicious activities, unauthorized access, and data breaches. The security mechanism is Intrusion Detection which is an important constituent that is employed to monitor and analyse the network traffic for recognizing and responding to intrusive or suspicious behavior. Innovative methods such as deep learning (DL) are employed to enhance the effectiveness of Intrusion Detection Systems (IDSs). DL is extremely implemented for IDS owing to its proficiency for automatically learning and extracting complex patterns and features from massive and multifaceted network datasets. Neural network (NN) models, permit the system to distinguish between anomalous patterns and normal network behaviors, increasing the accuracy of intrusion detection. The flexibility of DL methods to emerging cyberattacks with their adeptness to handle large - scale and various data, positions them as a strong and efficient tool for proactive and intelligent intrusion detection in existing cybersecurity settings. This article presents a Sine Cosine Algorithm with Optimal Convolutional Autoencoder for Intrusion Detection and Classification (SCAOCAE - IDC) method. The developed SCAOCAE - IDC system presents a wide - ranging strategy to improve the precision and effectiveness of IDSs. The method combines diverse advanced mechanisms like Min - Max scalar normalization for data preprocessing, Sine Cosine Algorithm (SCA) for feature selection (FS), Convolutional Autoencoder (CAE) for better feature extraction and classification, and Heap - Based Optimization (HBO) for hyperparameter tuning. The Min - Max scalar makes sure of robust data normalization, SCA increasingly chooses main features, CAE capably captures complex patterns in the data, and HBO fine - tunes hyperparameters for improved system performance. By employing the synergistic combination of such modules, the presented SCAOCAE - IDC algorithm indicates considerable outcomes for increasing the reliability and accuracy of IDSs and classification systems.

A review of feature selection strategies utilizing graph data structures and Knowledge Graphs.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.

Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum-paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

Quantized Orthogonal Experimentation SSA (QOX-SSA): A Hybrid Technique for Feature Selection (FS) and Neural Network Training

Quantized Orthogonal Experimentation SSA (QOX-SSA): A Hybrid Technique for Feature Selection (FS) and Neural Network Training

Bluefin Trevally Optimizer (BTO): A Metaheuristic Algorithm Using Fuzzy Logic Controller for Feature Selection Problem

Metaheuristic algorithms are increasingly used for feature selection (FS) problems due to their robustness and searchability. This study presents a unique optimization technique called fuzzy bluefin trevally optimizer (BTO). BTO draws its inspiration from bluefin’s cooperative nature and sudden ambush behavior. The trevally will jump out of the water and attack its food in the air or even snag it off the water when it is sufficiently close to it. This study uses mathematical modeling to create an optimization algorithm to replicate dynamic patterns and behaviors. Since parameters play a critical role in optimization, we use the fuzzy concept for dynamic parameter adaptation in fuzzy BTO. By introducing the idea of signature and demonstrating that fuzzy BTO converges to the global optimal point, the mathematical basis for the proposed algorithm is provided. The convergence of fuzzy BTO is proved using the Markov chain property. On benchmark functions (BF), the effectiveness of the suggested strategies is evaluated and compared with other metaheuristic algorithms. Then the proposed algorithms are applied to a fuzzy FS problem. Then they are compared with traditional and recent metaheuristic algorithms on the UCI machine learning repository datasets. Finally, the statistical significance is examined using the Kruskal–Wallis test (KWT).

FS-WOA-stacking: A novel ensemble model for early diagnosis of breast cancer

In this paper, a new integrated scheme is proposed to accurately predict breast cancer, help doctors make early diagnosis and treatment plans, and improve the prognosis of patients. We selects five mainstream machine learning models: support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). The Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) are used as datasets to investigate the predictive performance of these single and ensemble models. Then, we use multiple linear regression method for feature selection (FS), the experimental results show that the change of feature subset will significantly affect the performance of the model. The recall and f1-score of the five models are improved by 1.19% and 0.84% on average. After that, we apply whale optimization algorithm (WOA) to optimize the hyperparameters of the model to improve their prediction performance. In the best-case scenario, the model demonstrated improvements of 1.02% in accuracy and 1.82% in precision. In addition, we ensemble these models by stacking, investigate the performance changes of the ensemble model when different models are used as meta learners. Finally, the FS-WOA-Stacking model achieves 99.56% accuracy on WBCD and 99.65% accuracy on WDBC. Compared with the existing breast cancer prediction models, the performance of the proposed model is at an excellent level.

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

AbstractSoftware defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

Enhancing heart disease detection and classification using fuzzy logic and metaheuristic algorithms in a blockchain assisted healthcare environment

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.

Chaotic tumbleweed optimization algorithm with stacked deep learning based cyberattack detection in industrial CPS environment

Cyberattacks on cyber-physical systems (CPS) have led to severe concerns, and then it is most significant to identify the attacks in the initial phase. But, there are major problems that are resolved in this area; it contains the capability of the security method for detecting earlier unknown attacks. These challenges are resolved with system behaviour analysis manners and semi-supervised or unsupervised machine learning (ML) approaches. The efficacy of the attack recognition method is strongly dependent upon the databases utilized for training the ML approaches. Anomaly detection is the procedure of recognizing anomalous procedures, which could not equal the predictable system behaviour. It permits the recognition of novel and secret attacks. Presently, anomaly detection systems are frequently executed utilizing ML like shallow (or traditional) learning and deep learning (DL). This article develops a Chaotic Tumbleweed Optimization Algorithm with Stacked Deep Learning based Cyberattack Detection (CTOASDL-CD) approach in the Industrial CPS platform. The CTOASDL-CD technique intends to exploit the FS with an optimal hyperparameter-tuned DL model for the detection of cyberattacks. To resolve the high dimensionality issue, the CTOASDL-CD technique uses CTOA for feature selection (FS) purposes. Besides, the stacked deep belief network (SDBN) model can be utilized for the recognition of cyberattacks. Finally, the tunicate swarm algorithm (TSA) has been deployed for the capable selection of hyperparameter values of the SDBN approach. To determine the improved performance of the CTOASDL-CD approach, a wide-ranging simulation value was performed. The experimental values demonstrated the capable outcome of the CTOASDL-CD methodology in accomplishing security in the industrial CPS environment.

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%.

Intrusion detection in cloud computing based on time series anomalies utilizing machine learning

The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.

Feature Selection based nature inspired Capuchin Search Algorithm for solving classification problems

Identification of the optimal subset of features for Feature Selection (FS) problems is a demanding problem in machine learning and data mining. A trustworthy optimization approach is required to cope with the concerns involved in such a problem. Here, a Binary version of the Capuchin Search Algorithm (CSA), referred to as BCSA, was developed to select the optimal feature combination. Owing to the imbalance of parameters and random nature of BCSA, it may sometimes fall into the trap of an issue called local maxima. To beat this problem, the BCSA could be further improved with the resettlement of its individuals by adopting some methods of repopulating the individuals during foraging. Lévy flight was applied to augment the exploitation and exploration abilities of BCSA, a method referred to as LBCSA. A Chaotic strategy is used to reinforce search behavior for both exploration and exploitation potentials of BCSA, which is referred to as CBCSA. Finally, Lévy flight and chaotic sequence are integrated with BCSA, referred to as LCBCSA, to increase solution diversity and boost the openings of finding the global optimal solutions. The proposed methods were assessed on twenty-six datasets collected from the UCI repository. The results of these methods were compared with those of other FS methods. Overall results show that the proposed methods render more precise solutions in terms of accuracy rates and fitness scores than other methods.

MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets.

Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection (FS) and classification, combining a niche-based multiobjective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multiobjective EA to optimize the hyperparameters of the classifier and perform FS, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important nonredundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. https://github.com/PanKonstantinos/MEvA-X.

Feature Selection Using Games with Imperfect Information (FSGIN)

Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing GT based FS techniques are restricted to Zero Sum Games, Non-Zero Sum Games and Cooperative Games. The classical setting of assuming that all the details of players are known to all players cannot hold in many real-world problems. When the given features are independent, they cannot be treated alike and a characteristics based uncertainty persists among the features. This uncertainty is handled by none of the game forms used in the existing methods. Unlike the mentioned game techniques, Bayesian Games (BG) address the games with imperfect information. This paper investigate the FS problem in terms of BG and proposes a novel method to select the best features. The proposed BG based FS method is a filter type FS method and it starts with identifying Principle Features (PF) and proceeds to play global pairwise Bayesian games between those PF to obtain feature scores. Later, the features are ranked using these scores. In the final stage, a forward selection method with Support Vector Machine (SVM) is used to evaluate the classification performance of the ranked features and helps in the selection of the optimal set of features. Besides these, to improve the scalability of the proposed method MapReduce paradigm is exploited. In order to show the efficacy of the proposed method, experiments are carried out with seven real-world datasets from UCI and Statlog repositories. The promising results showed a significant improvement in the classification performance with fewer selected features than which is achieved using the existing methods.

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

One of the essential aims of intelligent algorithms concerning the diagnosis of heart disease is to achieve accurate results and discover valuable patterns. This paper proposes a new hybrid model based on Binary Farmland Fertility Algorithm (BFFA) and Naïve Bayes (NB) to diagnose heart disease. The BFFA is used for Feature Selection (FS) and the NB for data classification. FS can be employed to discover the most beneficial features. Four valid and universal UCI datasets (Heart, Cleveland, Hungary and Switzerland) were used to diagnose heart disease. Each dataset included 13 main features. The evaluation of the proposed model is simulated in MATLAB 2017b. The number of features in four datasets of Heart, Cleveland, Hungary and Switzerland is equal to 13, which was reduced to six for each dataset through the BFFA to better the efficiency of the proposed model. For evaluation, the accuracy criterion, the criterion of accuracy in the proposed model for all features in the four datasets Heart, Cleveland, Hungary and Switzerland, is equal to 82.25%, 86.91%, and 89.32% 89.24%, respectively. Results of the proposed model showed appropriateness in comparison to some other methods. In this paper, the proposed model was compared with other methods, and it was found out that the proposed model possessed a better accuracy percentage.

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and run-length are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field Algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

Expert cancer model using supervised algorithms with a LASSO selection approach

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

Prenatal exposure to criteria air pollutants and associations with congenital anomalies: A Lebanese national study

Maternal exposure to air pollution has been associated with a higher birth defect (BD) risk. Previous studies suffer from inaccurate exposure assessment methods, confounding individual-level variations, and classical analytical modelling. This study aimed to examine the association between maternal exposure to criteria air pollutants and BD risk. A total of 553 cases and 10,214 controls were identified from private and public databases. Two subgroups were then formed: one for a matched case-control design, and another for Feature Selection (FS) analysis. Exposure assessment was based on the mean air pollutant-specific levels in the mother’s residential area during the specific BD gestational time window of risk (GTWR) and other time intervals. Multivariate regression models outcomes consistently showed a significant protective effect for folic acid intake and highlighted parental consanguinity as a strong BD risk factor. After adjusting for these putative risk factors and other covariates, results show that maternal exposure to PM2.5 during the first trimester is significantly associated with a higher overall BD risk (OR:1.05, 95%CI:1.01–1.09), and with a higher risk of genitourinary defects (GUD) (OR:1.06, 95%CI:1.01–1.11) and neural tube defects (NTD) (OR:1.10, 95%CI:1.03–1.17) during specific GTWRs. Maternal exposure to NO2 during GTWR exhibited a significant protective effect for NTD (OR:0.94, 95%CI:0.90–0.99), while all other examined associations were not statistically significant. Additionally, maternal exposure to SO2 during GTWR showed a significant association with a higher GUD risk (OR:1.17, 95%CI:1.08–1.26). When limiting selection to designated monitor coverage radiuses, PM2.5 maintained significance with BD risk and showed a significant gene-environment interaction for GUD (p = 0.018), while NO2 protective effect expanded to other subtypes. On the other hand, FS analysis confirmed maternal exposure to PM2.5 and NO2 as important features for GUD, CHD, and NTD. Our findings, set the basis for building a novel BD risk prediction model.

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classification, statistical learning methods require hidden numerical features from the data. Furthermore, in the literature, a wealth of sequence intrinsic features has been proposed for ncRNA identification. The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial for the performance of any statistical learning method. To alleviate the posed challenges, we generated 96 feature datasets from ncRNA widely used features. The feature datasets are based on RNACentral and consist of species, ncRNA types, and expert databases that are available on the FexRNA platform. Additionally, the feature datasets are explored and analysed to provide statistical information, univariate, and bivariate analysis. We sought to determine which of these 17 features would be most appropriate to use in developing ncRNA classification approaches. For feature selection (FS), a two-phase hierarchical FS framework based on correlation and majority voting is proposed and evaluated on 5 species. The FexRNA platform provides information about ncRNA feature analysis and selection.

Dynamic Salp swarm algorithm for feature selection

Recently, many optimization algorithms have been applied for Feature selection (FS) problems and show a clear outperformance in comparison with traditional FS methods. Therefore, this has motivated our study to apply the new Salp swarm algorithm (SSA) on the FS problem. However, SSA, like other optimizations algorithms, suffer from the problem of population diversity and fall into local optima. To solve these problems, this study presents an enhanced version of SSA which is known as the Dynamic Salp swarm algorithm (DSSA). Two main improvements were included in SSA to solve its problems. The first improvement includes the development of a new equation for salps’ position update. The use of this new equation is controlled by using Singer's chaotic map. The purpose of the first improvement is to enhance SSA solutions' diversity. The second improvement includes the development of a new local search algorithm (LSA) to improve SSA exploitation. The proposed DSSA was combined with the K-nearest neighbor (KNN) classifier in a wrapper mode. 20 benchmark datasets were selected from the UCI repository and 3 Hadith datasets to test and evaluate the effectiveness of the proposed DSSA algorithm. The DSSA results were compared with the original SSA and four well-known optimization algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). From the obtained results, DSSA outperformed the original SSA and the other well-known optimization algorithms over the 23 datasets in terms of classification accuracy, fitness function values, the number of selected features, and convergence speed. Also, DSSA accuracy results were compared with the most recent variants of the SSA algorithm. DSSA showed a significant improvement over the competing algorithms in statistical analysis. These results confirm the capability of the proposed DSSA to simultaneously improve the classification accuracy while selecting the minimal number of the most informative features.