Bayes Classifier Research Articles

Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images

Recent developments have highlighted the critical role that computer-aided diagnosis (CAD) systems play in analyzing whole-slide digital histopathology images for detecting gastric cancer (GC). We present a novel framework for gastric histology classification and segmentation (GHCS) that offers modest yet meaningful improvements over existing CAD models for GC classification and segmentation. Our methodology achieves marginal improvements over conventional deep learning (DL) and machine learning (ML) models by adaptively focusing on pertinent characteristics of images. This contributes significantly to our study, highlighting that the proposed model, which performs well on normalized images, is robust in certain respects, particularly in handling variability and generalizing to different datasets. We anticipate that this robustness will lead to better results across various datasets. An expectation-maximizing Naïve Bayes classifier that uses an updated Gaussian Mixture Model is at the heart of the suggested GHCS framework. The effectiveness of our classifier is demonstrated by experimental validation on two publicly available datasets, which produced exceptional classification accuracies of 98.87% and 97.28% on validation sets and 98.47% and 97.31% on test sets. Our framework shows a slight but consistent improvement over previously existing techniques in gastric histopathology image classification tasks, as demonstrated by comparative analysis. This may be attributed to its ability to capture critical features of gastric histopathology images better. Furthermore, using an improved Fuzzy c-means method, our study produces good results in GC histopathology picture segmentation, outperforming state-of-the-art segmentation models with a Dice coefficient of 65.21% and a Jaccard index of 60.24%. The model’s interpretability is complemented by Grad-CAM visualizations, which help understand the decision-making process and increase the model’s trustworthiness for end-users, especially clinicians.

Constructing a Predictive Model for High School Students' Enrolment Results

Nowadays, data-informed decision-making can assist high-school educational decision-making, especially student test scores, and further education data, then these data can be used to inform decision-making in schools and boost overall school performance. We aimed to assess the relationships among high school students' admission scores, academic performance at school, and psychological tests in the school system. The relationships were used to predict the departmental fields and universities that students would select for advanced studies in the future. This research will utilize data mining algorithms, including Naïve Bayes, SVM, and Neural Network classifiers, to extract and analyze hidden patterns in students' academic records and credentials. When the Bayesian Network model was used to predict whether a student's university is public or private, our results showed 78.46% of the predicted results. Predicting the accuracy of the top three departmental fields, the SVM model achieved the highest performance with an accuracy of 69.77% .Given that students' academic performance varies across high schools, each school should develop its customized predictive model using only its students’ data. This will help students believe in and pursue their interests.

Prediction of Heart Disease Risk among Patients in Federal Medical Centre, Abeokuta Using Naïve Bayes

Heart disease is a silent killer, which can cause sudden death of individuals without obvious symptoms. The risk of heart disease is a sign in human beings that must not be neglected. Therefore, this study aimed to predict heart disease among patients in the Federal Medical Hospital Centre (FMC), Abeokuta. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 80% of the data for training and 20% for testing. In addition, a Decision Tree Algorithm (DT) model was used to compare the performance of the Naive Bayes model. The performances of the two models were evaluated using accuracy, sensitivity, ROC-AUC, specificity, precision, and the F1-score. The Naive Bayes model achieved an overall accuracy of 83.61%, precision of 89.29%, recall of 78.12%, F1-Score of 83.33%, ROC-AUC of 90%, sensitivity of 78.12%, and specificity of 89.66. On the other hand, they were compared with the Decision Tree (DT) model which achieved an overall accuracy of 75.41%, precision of 77.42%, recall of 75%, F1-Score of 76.19%, ROC-AUC of 84.54%, sensitivity of 75%, and specificity of 75.86%. Similarly, the confusion matrix for both analyses gave the correct classification of 25 and 22 cases of patients who have heart disease while their wrong classification was 7 and 10 cases of patients who have no heart disease respectively. Furthermore, the importance of features carried out on both showed that the most significant features are the maximum heart rate achieved, fasting blood sugar, resting blood pressure, and chest pain respectively for Naive Bayes and Decision Tree. The findings of the analysis showed that the Naive Bayes model outperformed the Decision Tree in every aspect of the analyses in predicting the risk of heart disease based on the data used and, it suggested that medical health insurance should consider incorporating predictive modelling techniques like Naive Bayes into their risk assessment algorithms, which can be of great use in the medical line.

Choosing the machine learning algorithm for detecting intrusions into IoT

Aim. The paper aims to improve the security of IoT devices by applying machine learning algorithms to detect attacks against IoT networks. The relevance of the goal is defined by the ever-growing number of such attacks around the world and the widespread use of IoT systems. The paper provides relevant statistical data. An analysis of the available papers showed that various methods were examined individually and were not compared to each other, so the aim of this paper that consists in identifying the most promising machine learning algorithm for detecting attacks against IoT networks is of relevance. Methods. The paper used the following machine learning methods to detect attacks against IoT networks: logistic regression, SVC, random forest, K-nearest neighbour method, k-means method, naive Bayes classifier, and variants of gradient boosting (XGBoost, AdaBoost, and CatBoost). The novelty consists in the comparison of the outputs of the supervised algorithms with the unsupervised K-means in the context of detection of attacks against IoT networks. The attack detection systems under development were trained using the UNSWNB15 dataset that contains data on nine types of attacks. The number of entries is more than 80 thousand. More than half of the entries deal with attacks. The methods were compared using a number of metrics. Results. An intrusion detection system was structurally defined and implemented. The stages of its operation include the analysis of input data and the output of final statistical data. The results show that the random forest algorithm is the best one out of those examined. The method also performs well in terms of learning speed. That means that the algorithm can be deployed and applied with the greatest success. Conclusions. This paper presents the results of comparing various machine learning algorithms in the context of IoT device intrusion detection. The accuracy and the ROC-AUC curve are used to evaluate the efficiency of the employed models. Having compared the models of the employed algorithms we found that the RandomForestClassifier model has the highest accuracy and a high AUC, which means that this algorithm is the most efficient in terms of IoT network intrusion detection. Further research will be dedicated to distinguishing between the types of attack.

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicenter study.

Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics model for the prediction of LNM in OPSCCs using contrast-enhanced computed tomography (CECT). A retrospective analysis included 279 OPSCC patients from three institutions. CECT images were used for handcrafted (HCR) and DL feature extraction. Dimensionality reduction for HCR features used recursive feature elimination and least absolute shrinkage and selection operator algorithms, whereas DL feature dimensionality reduction used variance-threshold and recursive feature elimination algorithms. Radiomics signatures were constructed using support vector machine, decision tree, random forest, k-nearest neighbor, gaussian naive bayes classifiers and light gradient boosting machine. A combined model was then constructed using the screened DL, HCR, and clinical features. The area under the receiver operating characteristic curve (AUC) served to quantify the model's performance, and calibration curves were utilized to assess its calibration. The combined model exhibited robust performance, achieving AUC values of 0.909 (95% CI: 0.861-0.957) in the training cohort, 0.884 (95% CI: 0.800-0.968) in the internal validation cohort, and 0.865 (95% CI: 0.791-0.939) in the external validation cohort. It outperformed both the clinical model and best-performing radiomics model. Moreover, calibration was deemed satisfactory. The combined model based on CECT demonstrates the potential to predict LNM in OPSCCs preoperatively, offering a valuable tool for more precise and tailored treatment strategies. This study presents a novel combined model integrating clinical factors with deep learning radiomics, significantly enhancing preoperative LNM prediction in OPSCC.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

Smart Hostel Allocation System Using Naïve Bayes Algorithm

This research explores the potential of automated personality classification using supervised machine learning techniques, with a specific focus on the Myers-Briggs Type Indicator (MBTI) for hostel allocation. The problem is the manual and resource-intensive allocation of hostel accommodations in educational institutions, leading to personality mismatches among roommates, which negatively impacts students' academic engagement and well-being. The aim of this research is to improve hostel room allocation by predicting student personality traits, thus minimizing personality mismatches among roommates. A Naïve Bayes classifier was applied to the MBTI personality dataset acquired from Kaggle. Various preprocessing techniques, such as tokenization, stop-word removal, and word stemming, were used to prepare the dataset for feature selection. CountVectorizer and TF-IDF methods were employed to convert text into numerical vectors for machine learning analysis. Additionally, SMOTE was implemented to address class imbalance within the dataset, ensuring better model performance. The Naïve Bayes classifier showed promising results, with scores achieved across all personality traits as 73% precision, 70% accuracy, F1 Score 70% and Recall 70% in predicting MBTI personality traits from input text. Visualizations and performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate the model's efficiency. The research findings suggest that supervised machine learning techniques can effectively predict personality traits from social media data, aiding in improved hostel room allocation and potentially benefiting broader applications in education and computational psychology. This system is recommended for educational institutions and residential complexes seeking efficient roommate pairing.

Comparative Analysis of K-Means and Naïve Bayes Algorithms for Predicting Students' Academic Performance

A student's performance is a victory statistic in higher education. The university's exceptional academic record strengthens its position as one of the prerequisites for a prestigious university. Teachers need to forecast and analyze student performance to pinpoint areas of weakness and improve academic standing. In academic settings, Coordination of computational tactics to improve workforce management and academic attainment is achieved through Educational Data Mining (EDM), a theory-based approach. Classification is a broadly connected technique in forecasting student performance based on diverse criteria. Machine learning algorithms are fundamental to knowledge disclosures, permitting precise performance projection and early student-identifiable proof. This study examines how well students perform academically using the Naïve Bayes classifier (NBC) model and the K-Means clustering approach. From supervised and unsupervised machine learning, two (2) algorithms with comparable operational capacity were selected. The labeled classes in the classifier correspond to the grades in the dataset. Records were gathered from 178 students (400 levels) in Adamawa State University Mubi's computer science departments in the 2022–2023 academic sessions. The training and testing sets of the dataset are divided into two groups, each with a percentage ratio of 30% and 70%. According to the results, the Naïve Bayes model has an accuracy of 92.6%, while the K-Means model has an accuracy of 38.9%.

Disaster tweet classification using enhanced salp swarm algorithm

Twitter and Facebook are widely recognized as crucial tools for situational information during disasters. Given that the classification of disaster related tweets is computationally challenging due to the high dimension of textual data caused by the redundant and irrelevant features. Hence for optimal feature selection (FS) and classification of disaster tweets, this work utilizes binary salp swarm algorithm (BSSA) and proposed two enhancements over it (PBcSSA). The commensalism phase from symbiotic organisms search (SOS) is integrated with BSSA to enhance its feature space searchability and then its parallel implementation is done using Apache Spark framework to reduce the execution time. The experiments were performed in a cross-disaster setting on nine groups of datasets including biological, earthquake, flood, hurricane, industrial, societal, transportation, wildfire, and environmental. The proposed PBcSSA combined with the Naive Bayes (NB) classifier in wrapper mode and its performance is compared with standard BSSA, binary sine cosine algorithm (BSCA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary whale optimization algorithm (BWOA). The experimental results reveal that the proposed PBcSSA outperforms other algorithms in disaster tweet classification and achieved highest average F1-score with lowest feature set in a reduced execution time.

Pengembangan Aplikasi Sistem Pakar Preferensi ‘Bahasa Cinta’ Manusia Berbasis Web

The purpose of this research is to produce a web-based expert system application to identify a person's love language using the nave Bayes classifier method. This expert system application to identify a person's love language is intended for people who have not realized how important it is to know which love language is relevant to them. This expert system application to identify a person's love language provides guidance in the form of questions about the type of love language. The answers to the questions asked will lead to the final decision on the results of the expert system diagnosis. Sources of expert system application knowledge base obtained from books and the internet. In making the application using the extreme programming software development method. Extreme programming consists of four stages, namely: planning, design, coding, and testing. The result of this research is the application of an expert system of love language identification that works on a web base.

Klasifikasi Data Mining Prediksi Penjualan dengan Metode Appriori

Abstract Databases stored on storage media are rarely used by most of their users and even within a certain period of time the data is deleted because it is considered trash and only fills up the storage media. This assumption is not entirely true, because in fact a large database can provide the information needed for various interests, both for business interests in making decisions and for science and research. The development of information and communication technology in this era often called the millennial, information and communication technology is also increasingly advanced and developing and cannot be avoided. Where the development and progress of information and communication technology is growing very rapidly, such as the need for data processing which is increasing every day and if left alone, the data will be useless. By using the Text Mining technique, the classification method, a sentiment will be known to be positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about Agu Ate Store. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the emotion icon conversion, the accuracy value can be increased to 98%.

Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: a 14-year prospective-based cohort study

IntroductionMetabolic syndrome is a chronic disease associated with multiple comorbidities. Over the last few years, machine learning techniques have been used to predict metabolic syndrome. However, studies incorporating demographic, clinical, laboratory, dietary, and genetic factors to predict the incidence of metabolic syndrome in Koreans are limited. In the present study, we propose a genome-wide polygenic risk score for the prediction of metabolic syndrome, along with other factors, to improve the prediction accuracy of metabolic syndrome.MethodsWe developed 7 machine learning-based models and used Cox multivariable regression, deep neural network (DNN), support vector machine (SVM), stochastic gradient descent (SGD), random forest (RAF), Naïve Bayes (NBA) classifier, and AdaBoost (ADB) to predict the incidence of metabolic syndrome at year 14 using the dataset from the Korean Genome and Epidemiology Study (KoGES) Ansan and Ansung.ResultsOf the 5440 patients, 2,120 were considered to have new-onset metabolic syndrome. The AUC values of model, which included sex, age, alcohol intake, energy intake, marital status, education status, income status, smoking status, dried laver intake, and genome-wide polygenic risk score (gPRS) Z-score based on 344,447 SNPs (p-value < 1.0), were the highest for RAF (0.994 [95% CI 0.985, 1.000]) and ADB (0.994 [95% CI 0.986, 1.000]).ConclusionsIncorporating both gPRS and demographic, clinical, laboratory, and seaweed data led to enhanced metabolic syndrome risk prediction by capturing the distinct etiologies of metabolic syndrome development. The RAF- and ADB-based models predicted metabolic syndrome more accurately than the NBA-based model for the Korean population.

Performance Comparison of Supervised Machine Learning Methods in Classifying Celestial Objects

In recent times, astronomy has entered a new era with rapidly growing data sources and advanced observation techniques. The construction of powerful telescopes has enabled the collection of spectral data from millions of celestial objects. However, the increasing number and variety of data have made it challenging to categorize these celestial objects. This study employs machine learning methods to address the fundamental problem of classifying stars, galaxies, and quasars in astronomy. The dataset underwent detailed preprocessing to identify effective features for classification. KNIME Analytics Platform was used for data analysis and visualization, facilitating rapid and efficient data analysis through its drag-and-drop interface. Among the machine learning methods used in our study—Decision Trees, Random Forest, and Naive Bayes—the highest accuracy rate of 97.86% was achieved with the Random Forest model. Notably, despite its lower overall performance compared to other models, the Naive Bayes classifier exhibited superior performance in distinguishing the STAR class, which is one of the study's interesting findings. Future studies aim to enhance model accuracy by using larger and more diverse datasets and exploring different machine learning algorithms. Additionally, the impact of deep learning methods on classification performance will be investigated.

A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury.

To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI). We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models-Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)-were developed and assessed using the training and validation datasets. The optimal model was selected based on its performance metrics and used to create a dynamic web-based nomogram. In a cohort of 858 TBI patients, the HAP incidence was 41.02%. LR was determined to be the optimal model with superior performance metrics including AUC, accuracy, and F1-score. Key predictive factors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and the Systemic Immune Response to Inflammation Index (SIRI). The nomogram developed based on these predictors demonstrated high predictive accuracy, with AUCs of 0.818 and 0.819 for the training and validation datasets, respectively. Decision curve analysis (DCA) and calibration curves validated the model's clinical utility and accuracy. We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.

Employee Attrition Prediction Using Machine Learning Techniques

Effective employees are regarded as the most precious assets and the foundation of any business. Companies invest a lot of money in staff training programs because they believe they will pay off in the long run. Therefore, it is crucial to keep a long-term, promising staff; as has been seen throughout the years, this is one of HR's most challenging duties. This study aims to determine the key variables influencing employee attrition and develop a machine learning model to predict employee attrition based on the variables given. This will make it easier for management to spot workers who are likely to quit, allowing them to take preventative measures and make wise decisions about appraisal and recognition. The four most popular classification algorithms utilized in this study were KNN, Naive Bayes, Random Forest and Logistic Regression. It was shown that Naive Bayes classifier outperformed the others by 89% in terms of accuracy and produced more accurate predictions.

Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Status Gizi Stunting Pada Balita

Child stunting is a major public health concern in Indonesia. This study uses the Naïve Bayes classification algorithm to assess the nutritional condition of stunted children based on demographic and anthropometric characteristics. The information used comes from the Toddler Weighing Month (Bulan Penimbangan Balita - BPB) in Majalengka Regency. Data type conversion, separating data into training and testing sets, and data normalization are all examples of preprocessing steps. The model's evaluation results reveal an accuracy of 94.65%, with precision and recall for each category of stunted nutritional status. This study makes a substantial contribution to early diagnosis and mitigation of stunting in Indonesia, as well as providing the framework for future development of more powerful predictive models.

Sentiment Analysis on the Impact of Artificial Intelligence (AI) Development to Determine Technology Needs

Artificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.

Chemometric Classification of Motor Oils Using 1H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

ABSTRACTHere, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.

Accelerometers-based position and time interval comparisons for predicting the behaviors of young bulls housed in a feedlot system

Animal behavior monitoring is an important tool for animal production. This behavior monitoring strategy can indicate the well-being and health of animals, which can lead to better productive performance. This study aimed to assess the most effective accelerometer attachment position (on either the halter or a neck collar) and data transmission time intervals (ranging from 6 to 600 s) for predicting behavioral patterns, including water and food intake frequencies, as well as other activities in young beef cattle bulls within a feedlot system. A range of machine learning algorithms were applied to satisfy the aims of the study, including the random forest, support vector machine, multilayer perceptron, and naive Bayes classifier algorithms. All studied models produced high performance metrics (above 0.90) when using both attachment positions, except for the models built using the naive Bayes classifier. Therefore, coupling accelerometers with collars is a more viable alternative for use on animals, as doing so is easier than applying accelerometers to halters. Utilizing a dataset with more observations (i.e., shorter time intervals) did not result in considerable improvements in the performance metrics of the trained models. Therefore, using datasets with fewer observations is more advantageous, as it can lead to decreased computational and temporal demands for model training, in addition to saving the battery of the device considered in this study.

Automatic similarity feature selection for ontology matching with semantic sampling

Ontology provides a formal representation of knowledge as a set of concepts within a domain and the relationships between them. Ontology Matching (OM) identifies similar entities across different ontologies. Similarity Features (SFs) characterize the similarity between two entities from different perspectives, which serves as the foundation of OM method. However, noisy and redundant SFs can obstruct the relevance of useful ones, reducing the quality of matching results. To offer a practical and efficient SF selection, this work proposes a new automatic SF selection framework for OM, which consists of two new components. First, a semantic sampling method is proposed to automatically construct a balanced and representative training dataset, without the need for expert intervention. This method first generates a basic sample set that considers both the representativeness of the concepts and the heterogeneous characteristics of the two ontologies, and then over-samples the minority samples based on their semantic context. Second, a wrapper-based SF selection method is designed, which includes an Adaptive Compact Genetic Algorithm (ACGA) and a Relaxed Naive Bayes (RNB) classifier. The ACGA is able to efficiently identify the SF subset, reducing the computational cost by adaptively maintaining a population probability model based on the diversity and confidence of the SFs. Moreover, the RNB can effectively evaluate the quality of selected SFs by modifying the feature independence assumption with recombination and weighting strategies. Experimental results on the ontology alignment evaluation initiative’s Benchmark demonstrate that our method can efficiently find accurate OM results, and significantly outperform the state-of-the-art matching techniques.