Performance Of Algorithm Research Articles

A new method to perform lithium-ion battery pack fault diagnostics – Part 2: the algorithm performance in real-world scenarios and cell-to-cell transferability

The last decade has witnessed significant progress towards zero-emission electric aviation. Lithium-ion batteries are at the centre of this technological transformation. However, electric flight requires suitable considerations for safety concerns associated with Lithium-ion batteries. Fault diagnostic approaches aimed at validating the safety of batteries before every flight have emerged as an attractive prospect. In this pursuit, a three-paper series proposing a novel fault diagnosis algorithm is presented. The algorithm is capable of diagnosing faults in an aircraft battery using the data collected during charging and was previously validated for a particular cell type under steady charging conditions. In this paper, two extensional aspects of the algorithm are investigated: cell-to-cell transferability and disparate charging conditions. While crucial for practical implementation, these aspects were overlooked in previous literature. Through experiments conducted at module-level, it was revealed that the algorithm could diagnose faults for a different cell type with minimal preliminary cell-level characterisation, thus demonstrating its ease of transferability. Moreover, modifications introduced in the algorithm enabled it to perform fault diagnosis under unsteady charging conditions for an aerospace module despite significant internal temperature gradients. Thus, the successful working of the algorithm in the considered aspects, which are unprecedented in literature, prove its feasibility for real-life application.

Balanced clustering for student admission school zoning by parameter tuning of constrained k-means

The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions

A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary’s karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs’ books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively.

State of health estimation embedded with hardware accelerator based on long short-term memory combined with Bayesian optimization considering extracted health indicator in charging conditions

Recently, the importance of renewable energy has increased as fossil fuel use is regulated worldwide to protect the environment. Accordingly, the use of electric vehicles (EVs) that use a battery as a power source is increasing and estimating the battery state for an efficient and safe operation of EV is a very significant task. This study proposes a method to estimate the state-of-health (SOH) in which batteries are a major state indicator. A health indicator (HI) capable of determining the state of the battery is derived from the charging part by considering the stopping situation in which there is a slight variation in the battery characteristics of the EV. As an integrity indicator, the partial capacity according to the voltage range set by the user can be considered, and the correlation between the SOH and HI is analyzed for validity verification. To learn the derived HI, a long short-term memory (LSTM) model, a type of deep-neural network, is designed, and model tuning was performed through Bayesian optimization. The absolute error of SOH estimation for the proposed LSTM model is 1.1 %, compared and validated against recurrent neural network (RNN) (1.7 %) and gated recurrent unit (GRU) (1.85 %). After validating the performance of the LSTM algorithm, an analysis of SOH estimation error (1 %) based on LSTM was conducted following pack-level experiments to assess scalability. Finally, for evaluation of operation within a hardware accelerator (HA) for integration into EVs, NVIDIA Jetson Nano was utilized. Upon mounting the algorithm on Jetson Nano, the mean absolute error (MAE) was 0.6581, mean absolute percentage error (MAPE) was 0.7209, and root mean square error (RMSE) was 0.7133, demonstrating excellent estimation performance.

Feature selection techniques for microarray dataset: a review

For many researchers working on feature selection techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create feature selection approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary feature selection techniques, such as Filter, Wrapper, and Embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on Critical Review Questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of feature selection strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment

Making a Better Logistic Regression Model Using Ensemble Learning

Abstract: Machine learning is a hot research topic in today's society and an important direction in the field of image classification research. Image classification is a supervised learning method used to classify images. There are many image classification algorithms that perform differently in different conditions. Paper focuses on improving the performance of image classification algorithms by using ensemble learning. Paper analyses the performance of logistic regression model and support vector machine model and then uses ensemble learning to increase the performance of logistic regression model. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of three algorithms when tested on same dataset, in same environment and on same system. The final results of the research prove that ensemble techniques give best results and this type of learning is effective.

Phishing Website Detection

Abstract: Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Our findings show that the model based on the K means clustering is the most accurate of the other four techniques and outperforms other solutions in the literature.

Performance of Phenotype Algorithms for the Identification of Opioid-Exposed Infants.

Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record data. We developed phenotype algorithms for the identification of opioid-exposed infants among a population of birthing person-infant dyads from an academic health care system (2010-2022). We derived phenotype algorithms from combinations of 6 unique indicators of in utero opioid exposure, including those from the infant record (NOWS or opioid-exposure diagnosis, positive toxicology) and birthing person record (opioid use disorder diagnosis, opioid drug exposure record, opioid listed on medication reconciliation, positive toxicology). We determined the positive predictive value (PPV) and 95% confidence interval for each phenotype algorithm using medical record review as the gold standard. Among 41 047 dyads meeting exclusion criteria, we identified 1558 infants (3.80%) with evidence of at least 1 indicator for opioid exposure and 32 (0.08%) meeting all 6 indicators of the phenotype algorithm. Among the sample of dyads randomly selected for review (n = 600), the PPV for the phenotype requiring only a single indicator was 95.4% (confidence interval: 93.3-96.8) with varying PPVs for the other phenotype algorithms derived from a combination of infant and birthing person indicators (PPV range: 95.4-100.0). Opioid-exposed infants can be accurately identified using electronic health record data. Our publicly available phenotype algorithms can be used to conduct research examining outcomes among opioid-exposed infants with and without NOWS.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning

AbstractObjectivesDocumentation and billing are important and time‐consuming parts of an otolaryngologist's work. Given advancements in machine learning (ML), we evaluated the ability of ML algorithms to use operative notes to classify rhinology procedures by Current Procedural Terminology (CPT®) code. We aimed to assess the potential for ML to replicate rhinologists' completion of their administrative tasks.Study DesignRetrospective cohort study.SettingUrban tertiary hospital.MethodsA total of 594 operative notes from rhinological procedures across six CPT codes performed from 3/2017 to 4/2022 were collected from 22 otolaryngologists. Text was preprocessed and then vectorized using CountVectorizer (CV), term frequency‐inverse document frequency, and Word2Vec. The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. Model‐classified CPT codes were compared to codes assigned by operating surgeons. Model performance was evaluated by area under the receiver operating characteristic curve (ROC‐AUC), precision, recall, and F1‐score.ResultsPerformance varied across vectorizers and ML algorithms. Across all performance metrics, CV and NB was most overall the best combination of vectorizer and ML algorithm across CPT codes and produced the single best AUC, 0.984.ConclusionsIn otolaryngology applications, the performance of basic ML algorithms varies depending on the context in which they are used. All algorithms demonstrated their ability to classify CPT codes well as well as the potential for using ML to replicate rhinologists' completion of their administrative tasks.

A data-driven decision-making approach for evaluating the projects according to resilience, circular economy and industry 4.0 dimension

Multi-industry organizations constantly define new projects for development and progression. However, the noteworthy point is that there are many options for defining new projects and allocating resources to them, from which the best choices must be selected among the candidate projects. In today's world, due to extensive changes in business conditions, traditional performance metrics are not suitable for project evaluation, and projects that can enhance organizational resilience and serve communities in line with economic and environmental components should be selected. Moreover, considering the widespread changes in digital transformation over the past decade, paying attention to Industry 4.0 standards in project selection is important. In this context, the main goal of this study is to design a data-driven decision-making model based on machine learning for evaluating and selecting projects in line with resilience, circular economy, and Industry 4.0 criteria. In the first step, 21 indicators were identified, and their weights were determined using the fuzzy best-worst method (FBWM). Then, using the weighted fuzzy inference system method (WFIS), rules for labeling data were established, and finally, a model for predicting and evaluating project performance was defined using the Light Gradient Boosting Machine algorithm. The findings indicate that the indicators “Increase market share using Industry 4.0 technologies” and “Net present value” are the most important in project evaluation. The developed model accurately evaluates projects with more than 93% accuracy, performing better than other algorithms and demonstrating the high performance of data-driven algorithms in project evaluation.

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Insufficient investigations have been conducted on the analysis of shield tunneling parameters and the prediction of the tunneling excavation speed in formations composed of volcanic ash strata. To address this issue, we employ a comprehensive approach utilizing literature research, mathematical statistics, and other methodologies, centered on the analysis of the No. 1 Tunnel of the Jakarta–Bandung High-Speed Railway. Our focus is on examining the evolution patterns and inter-relationships of shield tunneling parameters within volcanic ash strata. Subsequently, we propose an optimized strategy for these tunneling parameters. By employing six machine-learning algorithms to construct prediction models, we compare and analyze their performance in predicting the tunneling excavation speed. The results indicate a positive correlation between slurry pressure and tunnel depth in volcanic ash strata, suggesting that the grouting pressure should exceed the slurry pressure by approximately 0.22 MPa. In the composite stratum of “volcanic ash debris + round gravel”, the cutter torque exhibits a strong negative correlation with the total thrust (−0.77). Due to tool wear and ground resistance, the excavation speed and cutter speed are weakly negatively correlated. Compared to other strata, shield tunneling in volcanic ash strata exhibits larger grouting pressure fluctuations, slower tunneling excavation speed, greater total thrust, higher cutter torque, and lower cutter speed. Regarding shield tunneling excavation speed prediction, the ranking of the algorithm performance is RF > DNN > ANN > BPNN > MNR > SVM, with RF achieving a decision coefficient of 0.829. The RF model is well-suited for predicting the shield structure tunneling excavation speed.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

ObjectiveSuicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.MethodA systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.ResultsForty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing.The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction.Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.ConclusionsThe effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Application of Multi-Level Thresholding Fuzzy Entropy and Differential Evolution Techniques to Medical Image Compression

Each storage medium possesses a capacity that is vital to technological progress in supporting internet speed for downloading extensive data. Effective storage media management includes compressing image data to optimize available space utilization for efficient usage. Digital processing of image data for each patient over a specific duration is crucial. This study proposes medical image compression utilizing multi-level thresholding employing the fuzzy entropy and differential evolution (FE-DE) algorithms. The compression results are subsequently assessed using the Peak Signal to Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) to appraise the algorithm's performance and the quality of the compressed images. Employing Fuzzy Compression yields increasing PSNR, SSIM, and FSIM values as the threshold level rises, with the highest values achieved at threshold level 40. The increase in value is in line with the visual quality and compressed image produced.

Digitization Of Tulu Handwritten Scripts- A Literature Survey

Applications for handwritten script digitization include automated form processing, archival preservation, document digitization, handwriting recognition software, and historical document research. The performance of the recognition algorithms, the complex nature of the script, and the handwriting quality all affect how accurate the digitization is. Significant research on handwritten text recognition in scripts like Latin, Chinese, Arabic, and Japanese is reported. Still in its infancy, the work described on handwritten Indic scripts necessitates extensive research in this area. This thorough analysis offers an open view of different feature extraction and classification methods for the offline and online identification of handwritten Indic scripts.The primary objective of this paper is to explore the accuracy and efficiency of recognition systems by analyzing several state-of-the-art studies and comparing and evaluating various techniques for Tulu script handwritten character recognition. This paper aims to describe various advancements reported over the last few decades in the field of handwritten scripts recognition.

Kaczmarz Projection Algorithms with Rank Two Gain Update

AbstractNew Kaczmarz algorithms with rank two gain update, extended orthogonality property and forgetting mechanism which includes both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and systematically associated in this paper with well-known Kaczmarz algorithms with rank one update. The parameter convergence was proved using Lyapunov method and convergence of the inverse of the information matrix can be used for further performance improvement. The performance of new algorithms is examined in the problem of estimation of the grid events in the presence of significant harmonic emissions.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work.

Performance of a vectorcardiographic deep learning algorithm compared to single-lead and 12-lead ECG for atrial flutter detection: implications for wearable devices

Abstract Background Wearable devices have developed the capacity to offer single-lead ECG analysis and are already widespread in use, particularly for detection of atrial fibrillation (1,2). Vectorcardiography entails the transformation of electrical vectors into X, Y, and Z axes to constitute a vectorcardiogram (VCG) (3). The VCG has diagnostic promise given the capacity to generate it without requiring clinical presentation for standard 12-lead ECG acquisition. Furthermore, a novel credit-card sized device capable of acquiring a VCG has recently been developed – raising the potential for arrhythmia detection and discrimination beyond wearable single-lead ECG systems. Objective To assess the performance of a deep learning algorithm trained on single lead and 12-lead ECGs and VCGs for detecting atrial flutter. Methods A convolutional neural network (AFL-CNN) was developed on a dataset of 4,000 ECGs and VCGs from a single US academic medical institution (80% training and 20% for testing). Validation was performed on a separate publicly available dataset (2223 ECGs). Model performance was assessed on single lead ECG analysis (lead I, obtained by removing the other leads) and standard 12-lead ECG. The gold standard for AFL diagnosis was physician-confirmed 12-lead ECG diagnoses in the dataset. The VCG was generated from the 12-lead ECG via Kors transformation matrix to which AFL-CNN was also applied. Model performance was assessed with sensitivity, specificity, positive and negative predictive values, accuracy, and F1 statistic. Results On single lead ECG analysis, AFL-CNN achieved 71.2% sensitivity and 96.9% specificity with PPV 84.0% and NPV 93.7% and 92.2% accuracy with F1 83.2%. When AFL-CNN was applied to the VCG, performance improved: 91.0% sensitivity and 98.7% specificity, PPV 94.24%, NPV 98.0%, 97.3% accuracy, and F1 95.31%. Lastly, the performance remained high when AFL-CNN was applied to the 12-lead ECG: 95.4% sensitivity, 98.63% specificity, PPV 94.0%, NPV 99.0%, and accuracy of 98.0%, with F1 97.2%. Conclusion A deep learning algorithm applied to vectorcardiography for the detection of atrial flutter showed improved performance over single lead analysis and comparable performance to standard 12-lead applications. The finding of improved model performance beyond single lead ECG tracings underscores both the limitations of current wearable technologies in arrhythmia detection beyond atrial fibrillation and the potential for VCG-based algorithm prediction to fill gaps in healthcare inequality when standard 12-lead acquisition is challenging to obtain.

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.