Results 241 to 250 of about 40,856 (264)

Farthest SMOTE: A Modified SMOTE Approach

2018
Class imbalance problem comprises of uneven distribution of data/instances in classes which poses a challenge in the performance of classification models. Traditional classification algorithms produce high accuracy rate for majority classes and less accuracy rate for minority classes. Study of such problem is called class imbalance learning.
Anjana Gosain, Saanchi Sardana
openaire   +1 more source

An Oversampling Technique by Integrating Reverse Nearest Neighbor in SMOTE: Reverse-SMOTE

2020 International Conference on Smart Electronics and Communication (ICOSEC), 2020
In recent years, the classification problem of an imbalanced dataset is getting a high demand in the field of machine learning. The SMOTE (Synthetic Minority Oversampling Technique) is a traditional approach to solve this issue. The main drawback of SMOTE is the issue of overfitting, as it randomly synthesized the minority data samples taking no notice
Riju Das, Saroj Kr. Biswas, Debashree Devi, Biswajit Sarma   +3 more
openaire   +1 more source

Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

Information Sciences, 2019
Abstract Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications.
Georgios Douzas, Fernando Bacao
openaire   +1 more source

SMOTE-D a Deterministic Version of SMOTE

2016
Imbalanced data is a problem of current research interest. This problem arises when the number of objects in a class is much lower than in other classes. In order to address this problem several methods for oversampling the minority class have been proposed.
Fredy Rodríguez Torres, Jesús A. Carrasco-Ochoa, José Fco. Martínez-Trinidad   +2 more
openaire   +1 more source

SMOTE-Out, SMOTE-Cosine, and Selected-SMOTE: An enhancement strategy to handle imbalance in data level

2014 International Conference on Advanced Computer Science and Information System, 2014
The imbalanced dataset often becomes obstacle in supervised learning process. Imbalance is case in which the example in training data belonging to one class is heavily outnumber the examples in the other class. Applying classifier to this dataset results in the failure of classifier to learn the minority class. Synthetic Minority Oversampling Technique
openaire   +1 more source

HCAB-SMOTE: A Hybrid Clustered Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification

Arabian Journal for Science and Engineering, 2020
Binary datasets are considered imbalanced when one of their two classes has less than 40% of the total number of the data instances (i.e., minority class). Existing classification algorithms are biased when applied on imbalanced binary datasets, as they misclassify instances of minority class.
Hisham Al Majzoub, Islam Elgedawy, Öykü Akaydın, Mehtap Köse Ulukök   +3 more
openaire   +1 more source

SMOTE vs. ADASYN

Significant technical progress has led to an expansion in human requirements. As a result, the banking sector has seen a rise in the quantity of loan approval requests. When choosing a candidate for loan approval, a number of factors are taken into account to determine the loan's status.
Sabyasachi Pramanik, Soma Bag, Atanu Roy, Ramkrishna Ghosh, Pranati Rakshit   +4 more
openaire   +1 more source

Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)

Knowledge-Based Systems, 2023
Karim El Moutaouakil, Mouhamed Roudani, Abdellatif El Ouissari   +2 more
openaire   +1 more source

PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets

Neurocomputing, 2022
Qiong Chen, Zhong-Liang Zhang, Wen-Po Huang, Jian Wu, Xing-Gang Luo   +4 more
openaire   +1 more source

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

Progress in Nuclear Energy, 2017
Abstract Traditionally, the plight of imbalanced dataset and its classification quandary has been counteracted mostly using under-sampling, over-sampling or ensemble sampling methods. Among these algorithms, Synthetic Minority Over-sampling Technique (SMOTE) which belongs to oversampling method has had lot of admiration and extensive range of ...
Manas Ranjan Prusty, T. Jayanthi, K. Velusamy   +2 more
openaire   +1 more source