scholarly journals DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 557
Author(s):  
Alexandre M. de Carvalho ◽  
Ronaldo C. Prati
Keyword(s):  
Machine Learning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Sampling Technique ◽  
Classification Algorithms ◽  
Data Sets ◽  
Delaunay Tessellation ◽  
Minority Class ◽  
Imbalanced Data Sets

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. Since many real problems are unbalanced, this problem has become very relevant and deeply studied today. This paper presents a new preprocessing method based on Delaunay tessellation and the preprocessing algorithm SMOTE (Synthetic Minority Over-sampling Technique), which we call DTO-SMOTE (Delaunay Tessellation Oversampling SMOTE). DTO-SMOTE constructs a mesh of simplices (in this paper, we use tetrahedrons) for creating synthetic examples. We compare results with five preprocessing algorithms (GEOMETRIC-SMOTE, SVM-SMOTE, SMOTE-BORDERLINE-1, SMOTE-BORDERLINE-2, and SMOTE), eight classification algorithms, and 61 binary-class data sets. For some classifiers, DTO-SMOTE has higher performance than others in terms of Area Under the ROC curve (AUC), Geometric Mean (GEO), and Generalized Index of Balanced Accuracy (IBA).

SYNTHETIC OVERSAMPLING OF INSTANCES USING CLUSTERING

2013 ◽  
Vol 22 (02) ◽  
pp. 1350008 ◽  
Author(s):  
ATLÁNTIDA I. SÁNCHEZ ◽  
EDUARDO F. MORALES ◽  
JESUS A. GONZALEZ
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Minority Class ◽  
Imbalanced Data Sets ◽  
Tuning Parameters ◽  
New Methods ◽  
Real World Applications ◽  
Noisy Examples ◽  
F Measure ◽  
Better Than

Imbalanced data sets in the class distribution is common to many real world applications. As many classifiers tend to degrade their performance over the minority class, several approaches have been proposed to deal with this problem. In this paper, we propose two new cluster-based oversampling methods, SOI-C and SOI-CJ. The proposed methods create clusters from the minority class instances and generate synthetic instances inside those clusters. In contrast with other oversampling methods, the proposed approaches avoid creating new instances in majority class regions. They are more robust to noisy examples (the number of new instances generated per cluster is proportional to the cluster's size). The clusters are automatically generated. Our new methods do not need tuning parameters, and they can deal both with numerical and nominal attributes. The two methods were tested with twenty artificial datasets and twenty three datasets from the UCI Machine Learning repository. For our experiments, we used six classifiers and results were evaluated with recall, precision, F-measure, and AUC measures, which are more suitable for class imbalanced datasets. We performed ANOVA and paired t-tests to show that the proposed methods are competitive and in many cases significantly better than the rest of the oversampling methods used during the comparison.

scholarly journals Exploration of Obesity Status of Indonesia Basic Health Research 2013 With Synthetic Minority Over-Sampling Techniques

2021 ◽  
Vol 5 (1) ◽  
pp. 75-91
Author(s):  
Sri Astuti Thamrin ◽  
Dian Sidik ◽  
Hedi Kuswanto ◽  
Armin Lawi ◽  
Ansariadi Ansariadi
Keyword(s):  
Machine Learning ◽  
Health Research ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Sampling Technique ◽  
Data Sets ◽  
Data Set ◽  
Basic Health ◽  
Obesity Status ◽  
Obese Class

The accuracy of the data class is very important in classification with a machine learning approach. The more accurate the existing data sets and classes, the better the output generated by machine learning. In fact, classification can experience imbalance class data in which each class does not have the same portion of the data set it has. The existence of data imbalance will affect the classification accuracy. One of the easiest ways to correct imbalanced data classes is to balance it. This study aims to explore the problem of data class imbalance in the medium case dataset and to address the imbalance of data classes as well. The Synthetic Minority Over-Sampling Technique (SMOTE) method is used to overcome the problem of class imbalance in obesity status in Indonesia 2013 Basic Health Research (RISKESDAS). The results show that the number of obese class (13.9%) and non-obese class (84.6%). This means that there is an imbalance in the data class with moderate criteria. Moreover, SMOTE with over-sampling 600% can improve the level of minor classes (obesity). As consequence, the classes of obesity status balanced. Therefore, SMOTE technique was better compared to without SMOTE in exploring the obesity status of Indonesia RISKESDAS 2013.

scholarly journals Multi Labeled Imbalanced Data Classification Based on Advanced Min-Max Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 1776-1778
Keyword(s):  
Machine Learning ◽  
Uncertain Data ◽  
Imbalanced Data ◽  
Data Classification ◽  
Data Sets ◽  
Imbalanced Data Classification ◽  
Different Types ◽  
Measured System ◽  
Traditional Approaches

Some true applications, for example, content arrangement and sub-cell confinement of protein successions, include multi-mark grouping with imbalanced information. Different types of traditional approaches are introduced to describe the relation of hubristic and undertaking formations, classification of different attributes with imbalanced for different uncertain data sets. Here this addresses the issues by utilizing the min-max particular system. The min-max measured system can break down a multi-mark issue into a progression of little two-class sub-issues, which would then be able to be consolidated by two straightforward standards. Additionally present a few decay procedures to improve the presentation of min-max particular systems. Trial results on sub-cellular restriction demonstrate that our strategy has preferable speculation execution over customary SVMs in settling the multi-name and imbalanced information issues. In addition, it is additionally a lot quicker than customary SVMs

Sign in / Sign up

Export Citation Format

Share Document