A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Public complaint services are essential for improving government service quality by providing a direct channel for citizens to report issues. In Karawang Regency, the Tanggap Karawang (TANGKAR) platform serves this function; however, the manual classification of complaints causes delays and potential misrouting, especially due to the highly imbalanced distribution of complaint categories. This study develops an automatic classification model for public complaints in eight categories economy, education, health, social, infrastructure, security, environment, and transportation by integrating Term Frequency–Inverse Document Frequency (TF–IDF), Multinomial Naive Bayes, and Synthetic Minority Oversampling Technique (SMOTE). This integration addresses domain-specific class imbalance challenges, combining the computational efficiency of Naive Bayes, the feature representation strength of TF–IDF, and the improved minority class recognition from SMOTE. A dataset of 800 complaint records from TANGKAR underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stemming, and stopword removal. TF–IDF with unigram and bigram features was used for feature extraction, followed by classification under two scenarios: original unbalanced data and balanced data via SMOTE. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The model achieved 85.09% accuracy without SMOTE and 83.40% with SMOTE, with notable improvement in detecting minority categories after balancing. Although overall accuracy slightly decreased, SMOTE enhanced equitable prediction across all categories. This approach advances current public complaint classification methods by adapting to the linguistic diversity and uneven category distribution in actual e-government data, supporting faster and more accurate decision-making in public complaint management systems.

The usage of big data analytics is booming today, with its ability to be used to draw useful insights from past data research. Its uses in the aviation industry have a wide array of applications ranging from predicting flight delays to detecting faults in airplane parts. In this paper, we conducted exploratory data analysis on flight dataset to draw inferences on arrival and departure delays and to identify relationships between flight timings and delays. Using the flight delay data, we identified which flight is mostly prone to delays. The arrived upon conclusions are useful for selecting flights in the future.

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems.

In the semiconductor manufacturing industry, Automatic Defect Classification (ADC) plays an important role in maintaining high wafer inspection quality and reducing yield loss. ADC performance has benefitted from using machine learning (ML) algorithms; however, performance is negatively affected by the data imbalance and limited amounts of training data. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique to adjust the skewed class distribution of a dataset so that the bias of the majority class is reduced. This paper shows that applying SMOTE achieved higher accuracy and purity on two imbalanced datasets, consisting of scanning electron microscopy (SEM) images collected with ASML-HMI eP™ and eScan® series inspection tools. The ML models are also less sensitive to the selection of hyperparameters when SMOTE is applied. We also show that better classification results can be obtained with less training samples with SMOTE; we conducted an experiment where a ML model trained on only 25% of samples with SMOTE achieved a higher ADC accuracy and purity performance compared to the same ML model trained on all samples but without SMOTE. In another experiment using a highly imbalanced SEM dataset with very few counts of the defect-of- interest (DOI), the combination of SMOTE and random undersampling of the majority class improves the accuracy by up to 5x while maintaining the same level of purity.

According to the Indonesian Health Profile in 2017, diabetes is one of the causes of death for almost 70% in the world. The high mortality rate induces the need for making the effort to reduce the number of people with diabetes by conducting studies that lead to making a diagnosis so that can detect a person with diabetes accurately. This study tries to compare the performance of the K-Nearest Neighbors algorithm using Synthetic Minority Over-sampling Technique and the K-Nearest Neighbors algorithm without Synthetic Minority Over-sampling Technique in diagnosing diabetes on imbalanced datasets. The parameters tested are the k value of the K-Nearest Neighbors and Synthetic Minority Over-sampling Technique. The testing is carried out using the K-Fold Cross Validation strategy. The data used in this study were 3876 data from Pertamina Central Hospital. Based on the results of tests conducted, it shows that the value of accuracy produced in diagnosing diabetes by using Synthetic Minority Over-sampling Technique is better than the accuracy produced without using Synthetic Minority Over-sampling Technique with the highest accuracy increase of 8.25%. The highest average accuracy is obtained when the value of k = 3 in the K-Nearest Neighbors, k = 5 in the Synthetic Minority Over-sampling Technique, and fold = 10, which reaches 78.06%.

<p>Problem in imbalanced data is very common in classification or identification. The problem is raised when the number of instances of one class far exceeds the other. In the previous research, our DNA barcode-based Identification System of Tuna and Mackerel was developed in imbalanced dataset. The number of samples of Tuna and Mackerel were much more than those of other fish samples. Therefore, the accuracy of the classification model was probably still in bias. This research aimed at employing Synthetic Minority Oversampling Technique (SMOTE) to yield balanced dataset. We used kmers frequencies from DNA barcode sequences as features and Support Vector Machine (SVM) as classification method. In this research we used trinucleotide (3-mers) and tetranucleotide (4-mers). The training dataset was taken from Barcode of Life Database (BOLD). For evaluating the model, we compared the accuracy of model using SMOTE and without SMOTE in order to classify DNA barcode sequences which is taken from Department of Aquatic Product Technology, Bogor Agricultural University. The results showed that the accuracy of the model in the species level using SMOTE was 7% and 13% higher than those of non-SMOTE for trinucleotide (3-mers) and tetranucleotide (4-mers), respectively. It is expected that the use of SMOTE, as one of data balancing technique, could increase the accuracy of DNA barcode based fish classification system, particularly in the species level which is difficult to be identified.</p>

Bioassay data classification is an important task in drug discovery. However, the data used in classification is highly imbalanced, leading to inaccuracies in classification for the minority class. We propose a novel approach for classification in which we train separate models by using different features that are derived by training stacked autoencoders (SAE). Experiments are performed on 7 bioassay datasets, in which each data file consists of feature descriptors for every compound along with class label of compound being active, or inactive. We first perform data cleaning using borderline synthetic minority oversampling technique (SMOTE) followed by removing the Tomek links, and then learn different features hierarchically, based on the cleaned data or feature vectors. We then train separate cost-sensitive feed-forward neural network (FNN) classifiers using the hierarchical features in order to obtain the final classification. To increase the True Positive Rate (TPR), a test sample is labeled as active if at least one classifier predicts it as active. In this paper, we demonstrate that by data cleaning and learning separate classifiers one can improve the TPR and F1 score when compared to other machine learning approaches. To the best of our knowledge, the researchers have not yet attempted the use of SAE and FNN for classifying bioassay data.

Landslides are a significant natural disaster threat in Indonesia, including Samarinda City. This study aims to evaluate the classification accuracy of landslides using the Naïve Bayes algorithm, combined with the Synthetic Minority Oversampling Technique (SMOTE) for class imbalance and Particle Swarm Optimization (PSO) for feature Selection and parameter optimization. Data were obtained from BPBD and BMKG Samarinda for 2022-2024, evaluated using 10-Fold Cross Validation and Confusion Matrix. The results show that SMOTE did not always improve model accuracy. Naïve Bayes without SMOTE achieved an average accuracy of 87.18%, while with SMOTE, it decreased to 70.55%. This indicates that SMOTE’s impact depends on the dataset. PSO, used for feature Selection and parameter optimization, gave mixed results. Naïve Bayes + PSO Feature Selection achieved an average accuracy of 68.25%, lower than the base model with SMOTE. Naïve Bayes + PSO Optimization achieved the best accuracy at 71.03%, though the improvement was minimal. The combination of Naïve Bayes + PSO Feature Selection + PSO Optimization had the lowest accuracy at 68.08%. This study emphasizes that the use of PSO for feature Selection and optimization needs careful consideration, especially when combined with SMOTE, as its effect on model performance is not always optimal.

Synthetic Minority Oversampling Technique (SMOTE) is a preferable method used to solve the imbalanced data classification issues. However, its efficiency for resolving the issues of minority sample classification still need to be improved. In order to balance its value and shortcome, we designed a perfected algorithm called “C-K-SMOTE’', which is a mixture clustering algorithm of the Canopy and K-means. For the final purpose of obtaining an approximately balanced data, first we use Canopy to achieve the approximate clustering, then use the K-means to obtain the accurate clustering, and after that we apply the SMOTE to increase the number of minority samples. The referential imbalanced data sets used in the article are selected from KEEL (Knowledge Extraction on Evolutionary Learning). By adopting random forest disaggregated model to carry experiments, SMOTE's efficiency of balancing the imbalanced databases is verified.

Poverty data in official statistics data is important for development planning. The lower percentage of the poor recorded yearly indicates good development of a country. Moreover, there is always a problem when performing an inferential and classification analysis because of the imbalanced data, thereby leading to biases in the estimation results and prediction errors in the classification. One of the solutions to this problem is using Synthetic Minority Over-sampling Technique (SMOTE). Therefore, this study aims to evaluate the inference and classification quality using the binary logistic regression model without and with SMOTE. The data utilized was the poverty status of households in the rural and urban areas in East Java, Indonesia as contained in the 2019 National Socio-Economic Survey. Furthermore, the variables used are poverty status of the household, the age of the household head (HH), the ratio of household members who are employed, gender of the HH, number of household members, education level of HH, and occupation of the HH. It was concluded that the model with SMOTE approach was better at inference and classifying the results.

Most existing techniques to handle imbalanced data might be invalid in the presence of data missing since they are based on the assumption that the data is complete. To shorten such a gap, a novel synthetic minority oversampling technique (SMOTE), i.e., Non-negative latent factor analysis-incorporated and Switching triple-weight-SMOTE (NSS), is proposed. The main idea of NSS is 4-fold: 1) a Lagrange non-negative matrix factorization (LNMF) method is put forward to impute the missing values with a guaranteed non-negativity according to the original distribution owing to the consideration of the global feature information; 2) by mapping the complete data after imputation into an empirical feature space (EFS), a more separable dataset is achieved, which rigidly maintains the same geometrical structure as the original data while efficiently reducing the redundant features to enhance model generalization and operational efficiency; 3) after fulfilling the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$</tex-math> </inline-formula> -means (FCM) clustering, the inter-cluster distance, the capacity of each minority cluster and its sparsity are comprehensively taken into account to develop a triple-weight assignment strategy, which contributes to allocate the number of synthetic samples to each cluster appropriately; 4) a switching oversampling strategy is provided in response to the clusters with different distributions (i.e., either Gaussian or uniform distribution). Moreover, a posterior is used to check the correctness of the synthetic samples. Finally, experiments on a real dataset and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12$</tex-math> </inline-formula> public datasets show that the proposed NSS outperforms other <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11$</tex-math> </inline-formula> state-of-art methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Data classification is an important computer task, which has been successfully used in many domains, including but not limited to, the medical domain, finance domain, and manufacturing domain. However, there are two big challenges when a classifier is to handle real-world data in the presence of imbalanced data and missing values. More specifically, the model performance is very likely to be degraded due to the missing information and the inclination of classifiers to the majority class. To surmount this problem, a natural idea is to use the LNMF model to obtain the desired recovery. Then, for the complete data after imputation, the empirical-feature-space-based switching triple-weight-SMOTE is applied to synthesize safe and correct data (i.e., lie solidly on the region of minority class) to achieve the balance. Such a working principle generates a novel NSS strategy. The proposed NSS strategy has the following obvious merits: 1) the imputation guarantees the similarity to the original dataset; and 2) new synthetic data are safely generated by taking adequate consideration of the information and distribution of datasets. Thus, the proposed NSS can greatly help improve the classification accuracy of real-world datasets.

Wearable electroencephalography (EEG) devices for seizure detection accuracy and reliability are deep learning (DL) applications in the field of epilepsy diagnosis. In this study, we sought to increase the accuracy of seizure detection using advanced DL algorithms on the Children’s Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) EEG database. First, a fully convolutional network (FCN) was trained and assessed using accuracy and recall/precision metrics, and the early stopping technique was used to avoid overfitting. To assess the performance, the FCN was evaluated in terms of various metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC)-area under the curve (AUC). In addition, two-dimensional (2D) convolutional neural networks (CNNs) and long short-term memory (LSTM) models were used to model the database, and their performance was thoroughly measured using different metrics, graphs, and confusion matrices. Using LSTM variants, such as standard LSTM, bidirectional LSTM, stacked LSTM, and LSTM attention mechanisms, hybrid convolutional LSTM (ConvLSTM) models were trained and compared. The comparison was conducted based on the training and validation accuracy and loss, as well as the graphs resulting from the precision–recall curves. Apart from DL approaches, EEG signal analysis using time–frequency techniques, such as wavelet transform and short-time Fourier transform, has also been investigated. These methods assisted in the analysis of the time–frequency features of EEG signals in combination with DL models. This study demonstrates that the performance of wearable EEG devices can be augmented using a combination of DL and seizure signal processing techniques. The FCN achieved an accuracy of 92%, with a recall for seizures of 33%, an F1-score of 0.03, and strong ROC-AUC results. The 2D CNN achieved 96% accuracy, a seizure recall of 70%, an F1-score of 0.12, and an ROC-AUC score of 78%. The baseline LSTM struggled with effectiveness at 53% accuracy with a seizure recall of 18%. In contrast, the LSTM model, which incorporated synthetic minority oversampling technique (SMOTE) balancing, was able to reach improvements of up to 89% accuracy, with a precision of 91%, a recall of 86%, an F1-score of 0.89, and a strong ROC curve performance. Among the models, the LSTM with SMOTE was the best performer, with 89% accuracy, 91% precision, 86% recall, and an F1-score of 0.89. These results provide evidence that applying techniques for data balancing in combination with certain DL network architectures significantly improves the detection of seizures using wearable EEG devices worn on the body. We believe that real-time monitoring and high-performance systems are feasible using optimized DL frameworks. The analysis of the performance of different models allows for the understanding of the possibilities of optimizing the architectures of DL algorithms for the modern diagnosis of epilepsy in real time. The source code used to carry out the experiments is publicly available at CHB-MIT EEG Dataset Python Scripts (https://www.kaggle.com/code/adnankust/adnaneeg1).

Weighted-SMOTE: A modification to SMOTE for event classification in sodium cooled fast reactors

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.

Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.