scholarly journals Classification of minority class in imbalanced data sets

2022 ◽  
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Minority Class ◽  
Imbalanced Data Sets

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.

Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

2013 ◽  
Vol 42 ◽  
pp. 97-110 ◽  
Author(s):  
Alberto Fernández ◽  
Victoria López ◽  
Mikel Galar ◽  
María José del Jesus ◽  
Francisco Herrera
Keyword(s):  
Ad Hoc ◽  
Imbalanced Data ◽  
Data Sets ◽  
Imbalanced Data Sets

scholarly journals A Novel Weighted Ensemble Method to Overcome the Impact of Under-fitting and Over-fitting on the Classification Accuracy of the Imbalanced Data Sets

2021 ◽  
pp. 483-496
Author(s):  
Ghulam Fatima ◽  
Sana Saeed
Keyword(s):  
Data Mining ◽  
Imbalanced Data ◽  
Ensemble Method ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Imbalanced Data Sets ◽  
Sampling Procedures ◽  
The Impact

In the data mining communal, imbalanced class dispersal data sets have established mounting consideration. The evolving field of data mining and information discovery seeks to establish precise and effective computational tools for the investigation of such data sets to excerpt innovative facts from statistics. Sampling methods re-balance the imbalanced data sets consequently improve the enactment of classifiers. For the classification of the imbalanced data sets, over-fitting and under-fitting are the two striking problems. In this study, a novel weighted ensemble method is anticipated to diminish the influence of over-fitting and under-fitting while classifying these kinds of data sets. Forty imbalanced data sets with varying imbalance ratios are engaged to conduct a comparative study. The enactment of the projected method is compared with four customary classifiers including decision tree(DT), k-nearest neighbor (KNN), support vector machines (SVM), and neural network (NN). This evaluation is completed with two over-sampling procedures, an adaptive synthetic sampling approach (ADASYN), and a synthetic minority over-sampling (SMOTE) technique. The projected scheme remained efficacious in diminishing the impact of over-fitting and under-fitting on the classification of these data sets.

Sign in / Sign up

Export Citation Format

Share Document