Genetic Algorithm For Feature Selection Research Articles

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Novel hybrid data-driven models for enhanced renewable energy prediction

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.

Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection. This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers. Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%. The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.

Predicting Yield Strength and Plastic Elongation in Body-Centered Cubic High-Entropy Alloys.

We employ machine learning (ML) to predict the yield stress and plastic strain of body-centered cubic (BCC) high-entropy alloys (HEAs) in the compression test. Our machine learning model leverages currently available databases of BCC and BCC+B2 entropy alloys, using feature engineering to capture electronic factors, atomic ordering from mixing enthalpy, and the D parameter related to stacking fault energy. The model achieves low Root Mean Square Errors (RMSE). Utilizing Random Forest Regression (RFR) and Genetic Algorithms for feature selection, our model excels in both predictive accuracy and interpretability. Rigorous 10-fold cross-validation ensures robust generalization. Our discussion delves into feature importance, highlighting key predictors and their impact on mechanical properties. This work provides an important step toward designing high-performance structural high-entropy alloys, providing a powerful tool for predicting mechanical properties and identifying new alloys with superior strength and ductility.

Prevention of credit card fraud transaction using GA feature selection for web-based application

Credit card fraud (CCF) is a regular event that generates financial losses. A considerable share of the significantly increased volume of internet transactions is made with credit cards. CCF detection programmes are consequently highly prioritised by banks and other financial organisations. These fraudulent transactions can come in a wide variety of formats and categories. To maintain data integrity, financial institutions support digital transactions. One of the most popular ways to pay the products and services can be done by both online and offline by using a credit card. Thus, there is a higher possibility of fraud during these financial transactions. This informs programmers to the requirement for a reliable technique for identifying successful fraud. Credit card users and businesses that accept credit cards have recently had to contend with the serious issue of CCF. Application-level frauds and transaction level frauds are the two categories into which CCF controlled frauds are divided. Therefore, utilizing genetic algorithm (GA) feature selection for web-based applications, it is advised to use this strategy as a method for the prevention of CCF transaction. This method's performance is evaluated based on a number of factors, including accuracy, recall, and specificity.

GA-XGBoost, an explainable AI technique, for analysis of thrombin inhibitory activity of diverse pool of molecules and supported by X-ray

The present work involves extreme gradient boosting in combination with shapley values, a thriving amalgamation under the terrain of Explainable artificial intelligence, along with genetic algorithm for the analysis of thrombin inhibitory activity of diverse pool of 2803 molecules. The methodology involves genetic algorithm for feature selection, followed by extreme gradient boosting analysis. The eight parametric genetic algorithm - extreme gradient boosting analysis has high statistical acceptance with R2tr = 0.895, R2L10%O = 0.900, and Q2F3 = 0.873. Shapley additive explanations, which provide each variable in a model an importance value, served as the foundation for the interpretation. Then, ceteris paribus approach involving comparison of counterfactual examples has been used to understand the influence of a structural feature on activity profile. The analysis indicates that aromatic carbon, ring/non-ring nitrogen in combination with other structural features govern the inhibitory profile. The genetic algorithm - extreme gradient boosting model's simplicity and predictions suggest that “Explainable AI” is useful in the future for identifying and using structural features in drug discovery.

Using Optuna and Genetic Algorithm together facilitate the classification of plant leaf diseases

The identification and severity assessment of plant leaf diseases is crucial to food security and sustainable agriculture. This study shows an innovative way to improve plant leaf disease detection. The recommended method uses Optuna for parameter optimization and the Genetic Algorithm for feature selection to improve plant leaf disease identification. We do this to improve diagnosis accuracy. This method improves classification accuracy and is called ECPLDD-OGA. Modern hyper parameter optimization framework Optuna is employed. This allows classification model parameters to be fine-tuned. A systematic feature selection method is the Genetic Algorithm. It finds the most useful characteristics in the input dataset. By applying the algorithm on the data. By facilitation, the iterative process helps create a simplified and meaningful subset of features. Contrary to parameter tinkering and feature selection, empirical data suggests that utilizing Optuna and the Genetic Algorithm simultaneously improves disease identification. The updated model recognizes sick plants more accurately and generalizes better. Optimization enabled both gains. The usage of this technology can improve agricultural operations and reduce crop losses by increasing productivity. The present ECPLDD-OGA technique helps integrate hyper parameter tweaking and feature selection into machine learning-based agricultural applications.

Genetic algorithm feature selection resilient to increasing amounts of data imputation

This paper investigates the robustness of a genetic algorithm (GA) in feature selection across a dataset with increasing imputed missing values. Feature selection can be beneficial in predictive modeling to reduce computational costs and potentially improve performance. Beyond these benefits, it also enables a clearer understanding of the algorithm's decision-making processes. In the context of real-world datasets that can contain missing values, feature selection becomes more challenging.A robust feature selection algorithm should be able to identify the key features despite missing data values. We investigate the effectiveness of this approach against two other feature selection algorithms on a dataset with increasingly imputed values to determine whether it can sustain good performance with only the selected features.Our results reveal that compared to the other two methods, the features selected by GA resulted in better classification performance across different imputation rates and methods.

Using Genetic Algorithm Feature Selection to Optimize XGBoost Performance in Australian Credit

To reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset.

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.

Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.

Variable Selection for Recurrent Events Using Heuristic Approaches: Identifying Informative Variables for Rehospitalization in Schizophrenia Patients

Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
 Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
 Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
 Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.

Genetic algorithm for feature selection in mammograms for breast masses classification

ABSTRACT This paper introduces a Computer-Aided Detection (CAD) system for categorizing breast masses in mammogram images from the DDSM database as Benign, Malignant, or Normal. The CAD process involves Pre-processing, Segmentation, Feature Extraction, Feature Selection, and Classification. Three feature selection methods, namely the Genetic Algorithm (GA), t-test, and Particle Swarm Optimization (PSO) are used. In the classification phase, three machine learning algorithms (kNN, multiSVM, and Naive Bayes) are explored. Evaluation metrics like accuracy, AUC, precision, recall, F1-score, MCC, Dice coefficient, and Jaccard coefficient are used for performance assessment. Training and testing accuracy are assessed for the three classes. The system is evaluated using nine algorithm combinations, producing the following AUC values: GA+kNN (0.93), GA+multiSVM (0.88), GA+NB (0.91), t-test+kNN (0.91), t-test+multiSVM (0.86), t-test+NB (0.89), PSO+kNN (0.89), PSO+multiSVM (0.85), and PSO+NB (0.86). The study shows that the GA and kNN combination outperforms others.

Machine Learning Model For Stunting Prediction

This study aims to find the best Supervised Machine Learning (SML) model for stunting prediction. This research was conducted using an experimental approach using 192 infant data with a composition of 183 normal infant data and 9 stunted infant data using a custom dataset. The conclusion obtained from this study can be concluded that the combination of the Random Forest classification algorithm with Support Vector Machine Weighting and the Genetic Algorithm Feature Selection has the best performance. The parameters with the best performance are: The training and testing data distribution is 90% of the training data and 10% of the testing data. The number of trees in the random forest algorithm is 100, and the Gain Ratio criterion and max_depth is 10. In the Genetic Algorithm, the best parameters are: The Roulette Wheel selection method, the population is 20, the mutation value is 0.03, and the crossover value is 0.9. The validation method uses k-fold cross validation with a value of k = 10. Another conclusion is that there are 44 supporting factors for stunting, which, if we take a ranking of 10 in order of magnitude from largest to smallest, the supporting factors for stunting are 1.Baby's weight at birth. 2.Baby’s Height at Birth. 3.Number of meal per day. 4.Breast Milk. 5.Diarrhe times per 3 month. 6.Child development examination during covid by Health Worker at home. 7.Mother's age at birth. 8.Mother height at birth. 9.Number of sibling. 10.Age when the first food was given. This research has the disadvantage of no test on other datasets. So researchers do not know the reliability of findings is on different datasets

Chirplet transform based time frequency analysis of speech signal for automated speech emotion recognition

Nowadays, the recognition of emotion using the speech signal has gained popularity because of its vast number of applications in different fields like medicine, online marketing, online search engines, the education system, criminal investigations, traffic collisions, etc. Many researchers have adopted different methodologies to improve emotion classification accuracy using speech signals. In our study, time–frequency (TF) analysis-based features were used to analyze the emotion classification performance. We used a novel TF analysis method called the chirplet transform (CT) to find the TF matrix of the speech signal. We then calculated the proposed TF-based permutation entropy (TFPE) feature using the TF matrix of the speech signal. To reduce the feature dimension and select the most informative emotional feature, we employed the genetic algorithm (GA) feature selection method. Then, the selected TFPE features are used as input to machine learning classifiers such as SVM, RF, DT, and KNN to classify the emotions in the speech signal. We obtained classification accuracy of 77.2%, 69.57%, 68.78%, 56.9%, and 99.1% for the EMO-DB, EMOVO, SAVEE, IEMOCAP, and TESS datasets without the GA feature selection method. The emotion classification accuracy increased to 85.6%, 78.33%, 77.76%, 63.15%, and 100% with the GA feature selection method. We compared our results with other methods and found that our method performed better in emotion classification than the state-of-the-art methods.

High-Efficient Memristive Genetic Algorithm for Feature Selection

High-Efficient Memristive Genetic Algorithm for Feature Selection

Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.