Relevant bibliographies by topics / Synthetic minority oversampling technique

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Synthetic minority oversampling technique'

Author: Grafiati
Published: 2 June 2025
Last updated: 5 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Synthetic minority oversampling technique.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Synthetic minority oversampling technique"

1

Hooda, Sakshi, and Suman Mann. "Distributed Synthetic Minority Oversampling Technique." International Journal of Computational Intelligence Systems 12, no. 2 (2019): 929. http://dx.doi.org/10.2991/ijcis.d.190719.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Suci, Wulan, and Samsudin Samsudin. "Algoritma K-Nearest Neighbors dan Synthetic Minority Oversampling Technique dalam Prediksi Pemesanan Tiket Pesawat." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1775. http://dx.doi.org/10.30865/mib.v6i3.4374.

Full text
Abstract:
This study applies the Synthetic Minority Oversampling Technique to improve the performance of the K-Nearest Neighbors method in predicting the unbalanced data class. Most classification algorithms implicitly assume that the processed data has a balanced distribution, so that the standard classifier is more inclined towards data with a dominant class number (majority class). The use of Synthetic Minority Oversampling Technique can improve the performance of the K-Nearest Neighbors method for flight ticket booking data. Although in terms of accuracy, Synthetic Minority Oversampling Technique with K-Nearest Neighbors is lower at 79.65% compared to K-Nearest Neighbors without using Synthetic Minority Oversampling Technique, which is 97.81%, the suggested technique did not improve but from other performance, The proposed method can outperform K-Nearest Neighbors by using Synthetic Minority Oversampling Technique in terms of precision, recall, and F1-Score when applied to the Airline Ticket Booking dataset. Precision increased 18.00% from 62.00% to 80.00%, recall increased 28.00% from 52.00% to 80.00%, and F1-Score increased 27.00% from 53.00% to 80 ,00% on the flight ticket booking dataset.
APA, Harvard, Vancouver, ISO, and other styles
3

Rahardian, Hanif, Mohammad Reza Faisal, Friska Abadi, Radityo Adi Nugroho, and Rudy Herteno. "IMPLEMENTATION OF DATA LEVEL APPROACH TECHNIQUES TO SOLVE UNBALANCED DATA CASE ON SOFTWARE DEFECT CLASSIFICATION." Journal of Data Science and Software Engineering 1, no. 01 (2020): 53–62. http://dx.doi.org/10.20527/jdsse.v1i01.13.

Full text
Abstract:
Defects can cause significant software rework, delays, and high costs, to prevent disability it must be predictable the possibility of defects. To predict the disability the metrics software dataset is used. NASA MDP is one of the popular software metrics used to predict software defects by having 13 datasets and is generally unbalanced. The reward in the dataset can reduce the prediction of software defects because more unbalanced data produces a majority class. Data imbalance can be handled with 2 approaches, namely the data level approach technique and the algorithm level approach technique. The data level approach technique aims to improve class distribution by using resampling and data synthesis techniques. This research proposes a data level approach using resampling techniques, namely Random Oversampling (ROS), Random Undersampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), Tomek Link (TL) and One-Sided Selection (OSS) which are classified with Naïve Bayes was also validated using 10 Fold Cross-Validation, then evaluated with the Area Under ROC Curve (AUC). Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587. Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587. Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587.
APA, Harvard, Vancouver, ISO, and other styles
4

Gnip, Peter, Liberios Vokorokos, and Peter Drotár. "Selective oversampling approach for strongly imbalanced data." PeerJ Computer Science 7 (June 18, 2021): e604. http://dx.doi.org/10.7717/peerj-cs.604.

Full text
Abstract:
Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.
APA, Harvard, Vancouver, ISO, and other styles
5

Erlin, Erlin, Yenny Desnelita, Nurliana Nasution, Laili Suryati, and Fransiskus Zoromi. "Dampak SMOTE terhadap Kinerja Random Forest Classifier berdasarkan Data Tidak seimbang." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 21, no. 3 (2022): 677–90. http://dx.doi.org/10.30812/matrik.v21i3.1726.

Full text
Abstract:
Dalam aplikasi machine learning sangat umum ditemukan kumpulan data dalam berbagai tingkat ketidakseimbangan mulai dari ketidakseimbangan kecil, sedang sampai ekstrim. Sebagian besar model machine learning yang dilatih pada data tidak seimbang akan memiliki bias dengan memberikan tingkat akurasi yang tinggi pada kelas mayoritas dan sebaliknya rendah pada kelas minoritas. Tujuan penelitian ini adalah untuk mengevaluasi dampak dari SMOTE (Synthetic Minority Oversampling Technique) pada pengklasifikasi Random Forest untuk memprediksi penyakit jantung. Data berjumlah 299 berasal dari UCI Machine learning Repository digunakan untuk membangun model prediksi berdasarkan 12 variabel independen dan 1 variabel dependen. Kelas minoritas dalam dataset pelatihan di oversampling menggunakan teknik SMOTE (Synthetic Minority Oversampling Technique). Model dievaluasi tidak hanya menggunakan ukuran kinerja Accuracy dan Precision saja, namun juga menggunakan alternatif ukuran kinerja lainnya seperti Sensitivity, F1-score, Specificity, G-Mean dan Youdens Index yang lebih baik digunakan untuk data yang tidak seimbang. Hasil penelitian menunjukkan bahwa teknik SMOTE (Synthetic Minority Oversampling Technique) mampu mengurangi overfitting sekaligus meningkatkan kinerja model Random Forest pada semua indikator. Peningkatan skor Accuracy sebesar 3.45%, Precision 4.8%, Sensitivity 7.1%, F1-score 4.8%, Specificity 2.1%, G-Mean 4.4%, dan Youdens Index 6.3%. Penelitian ini membuktikan bahwa dalam menentukan pengklasifikasi dengan algoritma machine learning seperti Random Forest, kemiringan kelas dalam data perlu diperhitungkan dan diseimbangkan untuk hasil kinerja yang lebih baik.
APA, Harvard, Vancouver, ISO, and other styles
6

Vijayvargiya, Ankit, Aparna Sinha, Naveen Gehlot, Ashutosh Jena, Rajesh Kumar, and Kieran Moran. "S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality." PLOS ONE 19, no. 5 (2024): e0301263. http://dx.doi.org/10.1371/journal.pone.0301263.

Full text
Abstract:
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
APA, Harvard, Vancouver, ISO, and other styles
7

Viana, Diogo, Maria Teixeira, José Baptista, and Tiago Pinto. "Synthetic minority oversampling technique for synthetic meteorological data generation*." IET Conference Proceedings 2024, no. 29 (2025): 798–802. https://doi.org/10.1049/icp.2024.4759.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ai, Xusheng, Jian Wu, Victor S. Sheng, Pengpeng Zhao, and Zhiming Cui. "Immune Centroids Oversampling Method for Binary Classification." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/109806.

Full text
Abstract:
To improve the classification performance of imbalanced learning, a novel oversampling method, immune centroids oversampling technique (ICOTE) based on an immune network, is proposed. ICOTE generates a set of immune centroids to broaden the decision regions of the minority class space. The representative immune centroids are regarded as synthetic examples in order to resolve the imbalance problem. We utilize an artificial immune network to generate synthetic examples on clusters with high data densities, which can address the problem of synthetic minority oversampling technique (SMOTE), which lacks reflection on groups of training examples. Meanwhile, we further improve the performance of ICOTE via integrating ENN with ICOTE, that is, ICOTE + ENN. ENN disposes the majority class examples that invade the minority class space, so ICOTE + ENN favors the separation of both classes. Our comprehensive experimental results show that two proposed oversampling methods can achieve better performance than the renowned resampling methods.
APA, Harvard, Vancouver, ISO, and other styles
9

Hanifatul Azizah, Bagus Setya Rintyarna, and Triawan Adi Cahyanto. "Sentimen Analisis Untuk Mengukur Kepercayaan Masyarakat Terhadap Pengadaan Vaksin Covid-19 Berbasis Bernoulli Naive Bayes." BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer 3, no. 1 (2022): 23–29. http://dx.doi.org/10.37148/bios.v3i1.36.

Full text
Abstract:
Penelitian ini berisi tentang analisis sentimen masyarakat Indonesia pada Twitter terhadap kebijakan pemerintah dalam menangani kasus pandemi covid-19. Penelitian ini menggunakan metode Bernoulli Naive Bayes dalam melakukan pemodelan dan pengujian klasifikasi terhadap data sentimen. Digunakan juga metode pengukuran performa akurasi, presisi dan recall untuk mengukur performa metode Bernoulli Naive Bayes. Pada pembagian dan skenario pengujian digunakan teknik K Fold Cross Validation dengan nilai k = 2, 4, 5, 8 dan 10. ketidakseimbangan data dalam penelitian ini diselesaikan dengan menggunakan teknik Synthetic Minority Oversampling Technique (SMOTE). Dari hasil pengujian dengan model tanpa menggunakan teknik Synthetic Minority Oversampling Technique (SMOTE) diperoleh hasil dengan tingkat akurasi sebesar 80.58%, tingkat presisi sebesar 80.33% dan tingkat recall sebesar 85.57%. sedangkan hasil pengujian dengan menggunakan teknik Synthetic Minority Oversampling Technique (SMOTE) pada pemodelan, diperoleh tingkat akurasi 80.20%, tingkat presisi 78.04% dan tingkat recall 86.77%.
APA, Harvard, Vancouver, ISO, and other styles
10

Zhu, Tuanfei, Yaping Lin, and Yonghe Liu. "Synthetic minority oversampling technique for multiclass imbalance problems." Pattern Recognition 72 (December 2017): 327–40. http://dx.doi.org/10.1016/j.patcog.2017.07.024.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Synthetic minority oversampling technique"

1

Olaitan, Olubukola. "SCUT-DS: Methodologies for Learning in Imbalanced Data Streams." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37243.

Full text
Abstract:
The automation of most of our activities has led to the continuous production of data that arrive in the form of fast-arriving streams. In a supervised learning setting, instances in these streams are labeled as belonging to a particular class. When the number of classes in the data stream is more than two, such a data stream is referred to as a multi-class data stream. Multi-class imbalanced data stream describes the situation where the instance distribution of the classes is skewed, such that instances of some classes occur more frequently than others. Classes with the frequently occurring instances are referred to as the majority classes, while the classes with instances that occur less frequently are denoted as the minority classes. Classification algorithms, or supervised learning techniques, use historic instances to build models, which are then used to predict the classes of unseen instances. Multi-class imbalanced data stream classification poses a great challenge to classical classification algorithms. This is due to the fact that traditional algorithms are usually biased towards the majority classes, since they have more examples of the majority classes when building the model. These traditional algorithms yield low predictive accuracy rates for the minority instances and need to be augmented, often with some form of sampling, in order to improve their overall performances. In the literature, in both static and streaming environments, most studies focus on the binary class imbalance problem. Furthermore, research in multi-class imbalance in the data stream environment is limited. A number of researchers have proceeded by transforming a multi-class imbalanced setting into multiple binary class problems. However, such a transformation does not allow the stream to be studied in the original form and may introduce bias. The research conducted in this thesis aims to address this research gap by proposing a novel online learning methodology that combines oversampling of the minority classes with cluster-based majority class under-sampling, without decomposing the data stream into multiple binary sets. Rather, sampling involves continuously selecting a balanced number of instances across all classes for model building. Our focus is on improving the rate of correctly predicting instances of the minority classes in multi-class imbalanced data streams, through the introduction of the Synthetic Minority Over-sampling Technique (SMOTE) and Cluster-based Under-sampling - Data Streams (SCUT-DS) methodologies. In this work, we dynamically balance the classes by utilizing a windowing mechanism during the incremental sampling process. Our SCUT-DS algorithms are evaluated using six different types of classification techniques, followed by comparing their results against a state-of-the-art algorithm. Our contributions are tested using both synthetic and real data sets. The experimental results show that the approaches developed in this thesis yield high prediction rates of minority instances as contained in the multiple minority classes within a non-evolving stream.
APA, Harvard, Vancouver, ISO, and other styles
2

Shu-WeiLiao and 廖書緯. "A Local Information Based Synthetic Minority Oversampling Technique for Imbalanced Dataset Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5mdht9.

Full text
Abstract:
碩士<br>國立成功大學<br>工業與資訊管理學系<br>107<br>A dataset is imbalanced if the classes are not approximately equally represented. Data mining on imbalanced datasets receives more and more attentions in recent years. The class imbalanced problem occurs when there’s just few number of sample in one classes comparing to other classes. The SMOTE : Synthetic Minority Over-Sampling Technique is an effective method to solve imbalanced learning problem. The way is to take one of the minority sample as the seed sample, and find the minority sample nearby as the selected sample. After finding seed sample and selected sample, we generate virtual sample between two minority samples. Therefore, in this paper we consider the influence between majority samples and the selected sample and the influence between minority samples and the selected sample. This study develops a new sample-generating procedure by local majority class information and local minority class information. Four datasets taken from UCI Machine Learning Repository in experiments. We compare the proposed method with SMOTE and other extension version including Borderline SMOTE1(B1-SMOTE), Safe-Level SMOTE(SL-SMOTE), Local-Neighborhood SMOTE(LN-SMOTE), and ADASYN. The result shows that the proposed method achieve better classifier performance for the minority class than other methods after examined the data sets with C4.5 decision trees.
APA, Harvard, Vancouver, ISO, and other styles
3

Lin, Yi-Hsien, and 林宜憲. "Constructing a Credit Risk Assessment Model using Synthetic Minority Over-sampling Technique." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11786273799598686385.

Full text
Abstract:
碩士<br>國立交通大學<br>工業工程與管理學系<br>100<br>The main source of revenue of financial institutions is the interest they charge from their customers. But not all the customers will pay back their debt, financial institutions need to adopt some kind of risk assessment models in order to measure this credit risk. It is not uncommon to observe class imbalance problem in finance risk data. Class imbalance problem is asymmetric categories within data, that is, there is one class of data (major class) significantly outnumbered others (minor class). If we trained a model with imbalanced data, while the accuracy of major class instances might be very well, it could have a poor predictive ability to identify minority instances. Most of the risk assessment models apply sampling to deal with the class imbalanced problem. However, sampling method may lead to lack of data integrity and the model is sensitive on the sampling result as to produce inaccurate problems. This study constructs a risk model using Synthetic Minority Over-sampling Technique (SMOTE) to tackle class imbalance problems. The model we proposed not only fixed the lack of data integrity, but also solved the poor minority class predictive ability issue, hence improved the overall model accuracy. In the end, the study compares the results of classification with several sampling methods and previous Granular Computing model. By calculation and compare of the accuracy, AUC and G-means, we can conclude that using Synthetic Minority Over-sampling Technique to construct risk models would have the same or even better result than sampling and Granular Computing model.
APA, Harvard, Vancouver, ISO, and other styles
4

Chen, Shih-Cheng, and 陳世承. "An Improved Synthetic Minority Over-sampling Technique for Imbalanced Data Set Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9g74vs.

Full text
Abstract:
碩士<br>國立清華大學<br>資訊工程學系所<br>105<br>When a few categories of instances of a data set have fewer instances than other categories, such data sets may imply a problem of category imbalances, meaning that the trained classification model is likely to be found for a small number of instances Low cause, and a small number of instances of the wrong case to determine the majority of categories of examples. It is a solution to the distribution of imbalances between the majority of categories and the few categories through the artificial minority category data examples. A variety of algorithms have been designed based on this concept. This study proposes a novel algorithm ISMOTE to solve the problem of class imbalance. ISMOTE differs from previous algorithms in that it does not take into account only a few categories of distributions, but rather measures the relative advantages of a few categories and most categories in density distributions as a basis for weighting. In addition, our approach will choose to produce artificial instances with a few category instances and most of the nearest category instances as a reference instance. This approach can reduce the situation where the classifier's learning is more difficult due to the generation of erroneous man-made data instances, and the artificial examples through this approach can better help the classifier to learn. Each of the few category instances has a weight that the classifier has difficulty studying for this data instance. The design principles of the formula are proportional to the degree of difficulty in learning with this few categories of data instances. So ISMOTE can be for each of a few categories of data instances of the weight, resulting in the corresponding number of examples of artificial data and gradually change the boundaries of classification decisions to more difficult to learn the direction.
APA, Harvard, Vancouver, ISO, and other styles
5

鄒景隆. "Novel sampling methods based on synthetic minority over-sampling technique(SMOTE)for imbalanced data classification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/ek4vzp.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Limanto, Lisayuri, and 林芳婷. "A Hybrid Inference Model Based on Synthetic Minority Over-sampling Technique and Evolutionary Least Square SVM for Predicting Construction Contractor Default Status." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/46227772514646532070.

Full text
Abstract:
碩士<br>國立臺灣科技大學<br>營建工程系<br>101<br>Construction industry has several typical characteristics that are different compared to other economy sectors, including the dependability among project stakeholders. Thus, financial status of a construction company is an important issue in the construction industry. Assessing the financial status is challenging and the mapping relationship of input factors and the financial status of a company is very complicated. To avoid biased result and represent company’s financial condition, all available construction firm-years data in verified database center is employed in this study which caused imbalanced issue. This paper presents a hybrid inference model based on the financial ratios to estimate the contractor financial performance. The proposed model is constructed by combining Synthetic Minority Over-sampling Technique (SMOTE), Least Squares Support Vector Machine (LS-SVM), and Differential Evolution (DE). In the new model, SMOTE acts imbalanced dataset problem handling of non-default and default samples, LS-SVM is used as a supervised machine learning technique for classification, and DE is employed for specifying the optimal parameter of LS-SVM. A total record of 1695 construction contractor firm-years observations from 84 non-defaulted and 28 defaulted companies is collected and used to train and validate the proposed model. The Area Under the Curve (AUC) is utilized as the performance measurement of prediction results. As shown in the experimental results, the proposed models (AUC=0.98463) outperformed the other benchmark models, including Evolutionary Support Vector Machine Inference Model (ESIM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and logistic regression. Therefore, the proposed approach is a promising alternative for predicting contractor default status.
APA, Harvard, Vancouver, ISO, and other styles
7

Tsai, Meng-Fong, and 蔡孟峰. "Application and Study of imbalanced datasets base on Top-N Reverse k-Nearest Neighbor (TRkNN) coupled with Synthetic Minority Over-Sampling Technique (SMOTE)." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/38104987938865711006.

Full text
Abstract:
博士<br>國立中興大學<br>資訊科學與工程學系<br>105<br>The imbalanced classification means the dataset has an unequal class distribution among its population. For a given dataset without considering the imbalanced issue, most classification methods often predict the high accuracy for the majority class, but significantly low accuracy for the minority class. The first task in this dissertation is to provide an efficient algorithm, Top-N Reverse k-Nearest Neighbor (TRkNN), coupled with Synthetic Minority Over-Sampling TEchnique (SMOTE) to overcome this issue for several imbalanced datasets from famous UCI datasets. To investigate the proposed algorithm, it was applied into different classified methods, such as Logistic regression, C4.5, SVM, and BPNN. In addition, this research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrated that the Euclidean distance and Manhattan distance not only perform higher percentage of accuracy rate, but also show greater computational efficiency than the Chebyshev distance and Cosine distance. Therefore, the TRkNN and SMOTE based algorithm could be widely used to handle the imbalanced datasets and how to choose the suitable distance metrics can be as the reference for the future researches. Research into cancer prediction has applied various machine learning algorithms, such as neural networks, genetic algorithms, and particle swarm optimization, to find the key to classifying illness or cancer properties or to adapt traditional statistical prediction models to effectively differentiate between different types of cancers, and thus build prediction models that can allow for early detection and treatment. Training data from existing patients is used to establish models to predict the classification accuracy of new patient samples. This issue has attracted considerable attention in the field of data mining, and scholars have proposed various methods (e.g., random sampling and feature selection) to address category imbalances and achieve a re-balanced class distribution, thus improving the effectiveness of classifiers with limited data. Although resampling methods can quickly deal with the problem of unbalanced samples, they give more importance to the data in the majority class, and neglect potentially important data in the minority class, thus limiting the effectiveness of classification. Based on patterns discovered in imbalanced medical data sets, the second task in this dissertation is to use the synthetic minority oversampling technique to improve imbalanced data set issues. In addition, this research also compares the resampling performance of various methods based on machine learning, soft-computing, and bio-inspired computing, using three UCI medical data sets.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Synthetic minority oversampling technique"

1

Zięba, Maciej, Jakub M. Tomczak, and Adam Gonczarek. "RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barua, Sukarna, Md Monirul Islam, and Kazuyuki Murase. "A Novel Synthetic Minority Oversampling Technique for Imbalanced Data Set Learning." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_85.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Patel, Vibha, Jaishree Tailor, and Amit Ganatra. "Handling Class Imbalance in Electroencephalography Data Using Synthetic Minority Oversampling Technique." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sreelakshmi, S., and S. S. Vinod Chandra. "Landslide Classification Using Deep Convolutional Neural Network with Synthetic Minority Oversampling Technique." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24848-1_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Subudhi, Subhashree, Ram Narayan Patro, and Pradyut Kumar Biswal. "PSO-Based Synthetic Minority Oversampling Technique for Classification of Reduced Hyperspectral Image." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_48.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Xu, Shoukun, Zhibang Li, Baohua Yuan, Gaochao Yang, Xueyuan Wang, and Ning Li. "A No Parameter Synthetic Minority Oversampling Technique Based on Finch for Imbalanced Data." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4752-2_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

De Nicolò, Francesco, Marianna La Rocca, Antonio Marrone, et al. "Time Sensitive and Oversampling Learning for Systemic Crisis Forecasting." In New Economic Windows. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64916-5_9.

Full text
Abstract:
AbstractThe development of early warning systems for systemic crises has recently received growing interests. Recent studies have proposed possible solutions to address this challenging topic, in particular by means of cutting-edge artificial intelligence (AI) approaches. Financial data are fundamentally characterized by intrinsic temporal dynamics and the presence of both short-/long-term interactions. Hence, it is of paramount importance, when validating the proposed solutions to adopt validation strategies which consider this aspect. To this aim, we show here how Temporal Cross Validation (TCV) deeply affects the models’ learning. Moreover, to take into account the data imbalance often characterizing these models, we combine the TCV with a popular solution, which is the SMOTE (Synthetic Minority Oversampling TEchnique) algorithm.
APA, Harvard, Vancouver, ISO, and other styles
8

Zhou, Yan, Murat Kantarcioglu, and Chris Clifton. "On Improving Fairness of AI Models with Synthetic Minority Oversampling Techniques." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch98.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Diallo, Moussa, Abdoulaye Sidibé, and Djibril Diarra. "Imbalanced Data Classification Using Synthetic Minority Oversampling Technique in Stages for a Rice Dataset." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88226-5_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sinha, Ayush, Shubham Dwivedi, Sandeep Kumar Shukla, and O. P. Vyas. "Commissioning Random Matrix Theory and Synthetic Minority Oversampling Technique for Power System Faults Detection and Classification." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1648-1_43.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Synthetic minority oversampling technique"

1

Elmangoush, Abdullah M., Hanein O. Hassan, Ayyah A. Fadhl, and Malak Ahmed Alshrif. "Credit Card Fraud Detection Using Synthetic Minority Oversampling Technique and Deep Learning Technique." In 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP). IEEE, 2024. http://dx.doi.org/10.1109/atsip62566.2024.10638849.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Islam Sajol, Md Saiful, Imtiaz Ahmed, and Quazi Sanjid Mahmud. "Synthetic Minority Oversampling Technique Enhanced Machine Learning Models for Energy Theft Detection." In 2024 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2024. http://dx.doi.org/10.1109/kpec61529.2024.10676105.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

MuhsnHasan, Montater, Meghana A, Panjagari Kavitha, T. Aditya Sai Srinivas, and P. K. Chidambaram. "Predictive Maintenance for IoT-Enabled Wireless Devices Using AdaBoost with Synthetic Minority Oversampling Technique." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967754.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Saheed, Yakub Kayode, Sulaiman Olaniyi Abdulsalam, Mohammed Babatunde Ibrahim, and Usman Ahmad Baba. "Towards a New Hybrid Synthetic Minority Oversampling Technique for Imbalanced Problem in Software Defect Prediction." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800331.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sugitha, I. Putu Yoga Tunas, Fitra Abdurrachman Bachtiar, and Satrio Agung Wicaksono. "Application of Students Graduation Prediction Model Using Decision Tree C4.5 Algorithm and Synthetic Minority Oversampling Technique (SMOTE)." In 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2024. https://doi.org/10.1109/icvee63912.2024.10823806.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Saheed, Yakub Kayode, Oluwadamilare Harazeem Abdulganiyu, Mustapha Abdulsalam, Musa Mustapha, Mekila Mbayam Olivier, and Kaloma Usman Majikumna. "A Hybrid Ant Colony Optimization for Parkinson’s Disease Classification Based on Synthetic Minority Oversampling and Adaptive Synthetic Techniques." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800028.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hossain Raju, Md Azad, Touhid Imam, Jahirul Islam, Abdullah Al Rakin, Mohammad Navid Nayyem, and Mohammad Shihab Uddin. "An Ontological Framework for Lung Carcinoma Prognostication via Sophisticated Stacking and Synthetic Minority Oversampling Techniques." In 2024 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob). IEEE, 2024. https://doi.org/10.1109/apwimob64015.2024.10792946.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Santi, Rahmatika Pratama, Fajril Akbar, and Febby P. M. Piter. "A Comparative Study of Machine Learning Algorithm for Sentiment Analysis Using Word2Vec and Synthetic Minority Oversampling Technique (SMOTE) on COVID-19 Vaccination Program." In 2024 2nd International Symposium on Information Technology and Digital Innovation (ISITDI). IEEE, 2024. https://doi.org/10.1109/isitdi62380.2024.10796415.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Masud, Md Abdullah Al, Alazar Araia, Yuxin Wang, Jianli Hu, and Yuhe Tian. "Machine Learning-Aided Process Design for Microwave-Assisted Ammonia Production." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.121422.

Full text
Abstract:
Machine learning (ML) has become a powerful tool to analyze complex relationships between multiple variables and to unravel valuable information from big datasets. However, an open research question lies in how ML can accelerate the design and optimization of processes in the early experimental development stages with limited data. In this work, we investigate the ML-aided process design of a microwave reactor for ammonia production with exceedingly little experimental data. We propose an integrated approach of synthetic minority oversampling technique (SMOTE) regression combined with neural networks to quantitatively design and optimize the microwave reactor. To address the limited data challenge, SMOTE is applied to generate synthetic data based on experimental data at different reaction conditions. Neural network has been demonstrated to effectively capture the nonlinear relationships between input features and target outputs. The softplus activation function is used for a smoother prediction compared to the Rectified Linear Unit activation function. Ammonia concentration is predicted using pressure, temperature, feed flow rate, and feed composition ratio as input variables. For point-wise prediction based on discrete operating conditions, the proposed SMOTE integrated neural network approach outperforms with 96.1% accuracy compared to neural networks (without SMOTE), support vector regression, and linear regression. The multi-variate prediction trends are also validated which are critical for design optimization.
APA, Harvard, Vancouver, ISO, and other styles
10

K, Manjula Shenoy, and R. Srinivas Prabhu. "A comparative Analysis of ensemble methods and their efficiency in the classification of ‘HIT AND RUN’ cases in an imbalanced dataset (Traffic Crashes) with and without using "Synthetic minority oversampling technique"." In 2023 33rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2023. https://doi.org/10.1109/iccta60978.2023.10969264.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Synthetic minority oversampling technique"

1

Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

Full text
Abstract:
Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Synthetic minority oversampling technique' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography