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

scholarly journals K-Means Cluster Based Oversampling Algorithm for Imbalanced Data Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 3436-3440
Keyword(s):  
Machine Learning ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Problem ◽  
Disease Diagnosis ◽  
Skewed Data ◽  
Challenging Problem ◽  
Classification Problems ◽  
Imbalanced Data Classification

Imbalanced data classification problems endeavor to find a dependent variable in a skewed data distribution. Imbalanced data classification problems present in many application areas like, medical disease diagnosis, risk management, fault-detection, etc. It is a challenging problem in the field of machine learning and data mining. In this paper, K-Means cluster based oversampling algorithm is proposed to solve the imbalanced data classification problem. The experimental results show that the proposed algorithm outperforms the existing oversampling algorithms of previous studies.

scholarly journals A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Qing-Yan Yin ◽  
Jiang-She Zhang ◽  
Chun-Xia Zhang ◽  
Nan-Nan Ji
Keyword(s):  
Evaluation Criteria ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Data Classification ◽  
Data Sets ◽  
Imbalanced Data Sets ◽  
Ensemble Pruning ◽  
Imbalanced Data Classification ◽  
Selective Ensemble ◽  
Nonparametric Statistical

Learning with imbalanced data is one of the emergent challenging tasks in machine learning. Recently, ensemble learning has arisen as an effective solution to class imbalance problems. The combination of bagging and boosting with data preprocessing resampling, namely, the simplest and accurate exploratory undersampling, has become the most popular method for imbalanced data classification. In this paper, we propose a novel selective ensemble construction method based on exploratory undersampling,RotEasy, with the advantage of improving storage requirement and computational efficiency by ensemble pruning technology. Our methodology aims to enhance the diversity between individual classifiers through feature extraction and diversity regularized ensemble pruning. We made a comprehensive comparison between our method and some state-of-the-art imbalanced learning methods. Experimental results on 20 real-world imbalanced data sets show thatRotEasypossesses a significant increase in performance, contrasted by a nonparametric statistical test and various evaluation criteria.

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).

Sign in / Sign up

Export Citation Format

Share Document