Results 11 to 20 of about 40,856 (264)
SMOTE-CD: SMOTE for compositional data.
PLoS ONE, 2023 Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced.
Teo Nguyen +3 more
doaj +5 more sources
RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning.
Ahmed Arafa +3 more
doaj +2 more sources
Two Novel SMOTE Methods for Solving Imbalanced Classification Problems
IEEE Access, 2023 The imbalanced classification problem has always been one of the important challenges in neural network and machine learning. As an effective method to deal with imbalanced classification problems, the synthetic minority oversampling technique (SMOTE ...
Yuan Bao, Sibo Yang
doaj +1 more source
DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets
Information, 2020 One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others.
Alexandre M. de Carvalho +1 more
doaj +1 more source
Machine learning approach to customer sentiment analysis in twitter airline reviews [PDF]
E3S Web of Conferences, 2023 Customers typically provide both online and physical services they use ratings and reviews. However, the volume of reviews might grow very quickly. The power of machine learning to recognize this kind of data is astounding. Numerous algorithms that could
Pujo Ariesanto Akhmad Ekka +2 more
doaj +1 more source
, 2022 Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples.
Elor, Yotam, Averbuch-Elor, Hadar
openaire +2 more sources
Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark
Neurocomputing, 2021 One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an ...
Juez Gil, Mario +4 more
openaire +3 more sources
Barekeng, 2023 Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable.
Nila Lestari +3 more
doaj +1 more source
AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE [PDF]
Scientific Programming, 2020 Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm—AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE—is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority ...
Xin Wang +6 more
openaire +1 more source
Barekeng, 2023 Anemia and iron deficiency are health problems in Indonesia and globally. In Multiclass Classification, data problems often occur, such as missing data, too many variables, and unbalanced data.
Fathu Nurrahman +3 more
doaj +1 more source