Machine learning from imbalanced data sets for astronomical object classification

2011 ◽  
Author(s):  
Jorge de la Calleja ◽  
Antonio Benitez ◽  
Ma. Auxilio Medina ◽  
Olac Fuentes
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Object Classification ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Astronomical Object

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).

Machine Learning from imbalanced data-sets: an application to the bike-sharing inventory problem

2021 ◽  
Author(s):  
Giovanni Ceccarelli ◽  
Guido Cantelmo ◽  
Marialisa Nigro ◽  
Constantinos Antoniou
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Data Sets ◽  
Inventory Problem ◽  
Imbalanced Data Sets ◽  
Bike Sharing

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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