scholarly journals A Novel Approach for Handling Outliers in Imbalanced Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 1 ◽  
Author(s):  
Gillala Rekha ◽  
V Krishna Reddy
Keyword(s):  
Class Imbalance ◽  
Training Data ◽  
Classification Algorithms ◽  
Main Concern ◽  
Data Generation ◽  
Class Imbalance Problem ◽  
Class Distribution ◽  
Novel Approach ◽  
Real World Datasets ◽  
Data Level

Most of the traditional classification algorithms assume their training data to be well-balanced in terms of class distribution. Real-world datasets, however, are imbalanced in nature thus degrade the performance of the traditional classifiers. To solve this problem, many strategies are adopted to balance the class distribution at the data level. The data level methods balance the imbalance distribution between majority and minority classes using either oversampling or under sampling techniques. The main concern of this paper is to remove the outliers that may generate while using oversampling techniques. In this study, we proposed a novel approach for solving the class imbalance problem at data level by using modified SMOTE to remove the outliers that may exist after synthetic data generation using SMOTE oversampling technique. We extensively compare our approach with SMOTE, SMOTE+ENN, SMOTE+Tomek-Link using 9 datasets from keel repository using classification algorithms. The result reveals that our approach improves the prediction performance for most of the classification algorithms and achieves better performance compared to the existing approaches.   

A Novel Minority Cloning Technique for Cost-Sensitive Learning

2015 ◽  
Vol 29 (04) ◽  
pp. 1551004 ◽  
Author(s):  
Liangxiao Jiang ◽  
Chen Qiu ◽  
Chaoqun Li
Keyword(s):  
Class Imbalance ◽  
Training Data ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Cost Sensitive Learning ◽  
Class Distribution ◽  
Imbalance Problem ◽  
Cloning Technique ◽  
Replacement Technique ◽  
Better Than

In many real-world applications, it is often the case that the class distribution of instances is imbalanced and the costs of misclassification are different. Thus, the class-imbalanced cost-sensitive learning has attracted much attention from researchers. Sampling is one of the widely used techniques in dealing with the class-imbalance problem, which alters the class distribution of instances so that the minority class is well represented in the training data. In this paper, we propose a novel Minority Cloning Technique (MCT) for class-imbalanced cost-sensitive learning. MCT alters the class distribution of training data by cloning each minority class instance according to the similarity between it and the mode of the minority class. The experimental results on a large number of UCI datasets show that MCT performs much better than Minority Oversampling with Replacement Technique (MORT) and Synthetic Minority Oversampling TEchnique (SMOTE) in terms of the total misclassification costs of the built classifiers.

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

An Investigation of Imbalanced Ensemble Learning Methods for Cross-Project Defect Prediction

2019 ◽  
Vol 33 (12) ◽  
pp. 1959037 ◽  
Author(s):  
Shaojian Qiu ◽  
Lu Lu ◽  
Siyu Jiang ◽  
Yang Guo
Keyword(s):  
Ensemble Learning ◽  
Class Imbalance ◽  
Training Data ◽  
Defect Prediction ◽  
Class Imbalance Problem ◽  
Learning Methods ◽  
Imbalance Problem ◽  
Intelligent Software ◽  
Under Sampling ◽  
Cross Project

Machine-learning-based software defect prediction (SDP) methods are receiving great attention from the researchers of intelligent software engineering. Most existing SDP methods are performed under a within-project setting. However, there usually is little to no within-project training data to learn an available supervised prediction model for a new SDP task. Therefore, cross-project defect prediction (CPDP), which uses labeled data of source projects to learn a defect predictor for a target project, was proposed as a practical SDP solution. In real CPDP tasks, the class imbalance problem is ubiquitous and has a great impact on performance of the CPDP models. Unlike previous studies that focus on subsampling and individual methods, this study investigated 15 imbalanced learning methods for CPDP tasks, especially for assessing the effectiveness of imbalanced ensemble learning (IEL) methods. We evaluated the 15 methods by extensive experiments on 31 open-source projects derived from five datasets. Through analyzing a total of 37504 results, we found that in most cases, the IEL method that combined under-sampling and bagging approaches will be more effective than the other investigated methods.

scholarly journals An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

2019 ◽  
Vol 16 (1) ◽  
pp. 155-178 ◽  
Author(s):  
Kristina Andric ◽  
Damir Kalpic ◽  
Zoran Bohacek
Keyword(s):  
Credit Risk ◽  
Sample Size ◽  
Real Life ◽  
Class Imbalance ◽  
Performance Measure ◽  
Classification Algorithms ◽  
Data Sets ◽  
Valuable Insight ◽  
Data Set ◽  
Class Distribution

In this paper we investigate the role of sample size and class distribution in credit risk assessments, focusing on real life imbalanced data sets. Choosing the optimal sample is of utmost importance for the quality of predictive models and has become an increasingly important topic with the recent advances in automating lending decision processes and the ever growing richness in data collected by financial institutions. To address the observed research gap, a large-scale experimental evaluation of real-life data sets of different characteristics was performed, using several classification algorithms and performance measures. Results indicate that various factors play a role in determining the optimal class distribution, namely the performance measure, classification algorithm and data set characteristics. The study also provides valuable insight on how to design the training sample to maximize prediction performance and the suitability of using different classification algorithms by assessing their sensitivity to class imbalance and sample size.

scholarly journals CoGBUS- Center of Gravity based under Sampling Method for Imbalanced Data Classification

2019 ◽  
Vol 8 (2) ◽  
pp. 2463-2468
Keyword(s):  
Learning Community ◽  
Sampling Method ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Center Of Gravity ◽  
Classification Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Imbalanced Data Classification ◽  
Under Sampling

Learning of class imbalanced data becomes a challenging issue in the machine learning community as all classification algorithms are designed to work for balanced datasets. Several methods are available to tackle this issue, among which the resampling techniques- undersampling and oversampling are more flexible and versatile. This paper introduces a new concept for undersampling based on Center of Gravity principle which helps to reduce the excess instances of majority class. This work is suited for binary class problems. The proposed technique –CoGBUS- overcomes the class imbalance problem and brings best results in the study. We take F-Score, GMean and ROC for the performance evaluation of the method.

HCBST: An Efficient Hybrid Sampling Technique for Class Imbalance Problems

2022 ◽  
Vol 16 (3) ◽  
pp. 1-37
Author(s):  
Robert A. Sowah ◽  
Bernard Kuditchar ◽  
Godfrey A. Mills ◽  
Amevi Acakpovi ◽  
Raphael A. Twum ◽  
...  
Keyword(s):  
Geometric Mean ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Data Repository ◽  
Support Vector ◽  
Classification Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
High Degree ◽  
Hybrid Sampling

Class imbalance problem is prevalent in many real-world domains. It has become an active area of research. In binary classification problems, imbalance learning refers to learning from a dataset with a high degree of skewness to the negative class. This phenomenon causes classification algorithms to perform woefully when predicting positive classes with new examples. Data resampling, which involves manipulating the training data before applying standard classification techniques, is among the most commonly used techniques to deal with the class imbalance problem. This article presents a new hybrid sampling technique that improves the overall performance of classification algorithms for solving the class imbalance problem significantly. The proposed method called the Hybrid Cluster-Based Undersampling Technique (HCBST) uses a combination of the cluster undersampling technique to under-sample the majority instances and an oversampling technique derived from Sigma Nearest Oversampling based on Convex Combination, to oversample the minority instances to solve the class imbalance problem with a high degree of accuracy and reliability. The performance of the proposed algorithm was tested using 11 datasets from the National Aeronautics and Space Administration Metric Data Program data repository and University of California Irvine Machine Learning data repository with varying degrees of imbalance. Results were compared with classification algorithms such as the K-nearest neighbours, support vector machines, decision tree, random forest, neural network, AdaBoost, naïve Bayes, and quadratic discriminant analysis. Tests results revealed that for the same datasets, the HCBST performed better with average performances of 0.73, 0.67, and 0.35 in terms of performance measures of area under curve, geometric mean, and Matthews Correlation Coefficient, respectively, across all the classifiers used for this study. The HCBST has the potential of improving the performance of the class imbalance problem, which by extension, will improve on the various applications that rely on the concept for a solution.

CLASSIFICATION OF IMBALANCED DATA: A REVIEW

2009 ◽  
Vol 23 (04) ◽  
pp. 687-719 ◽  
Author(s):  
YANMIN SUN ◽  
ANDREW K. C. WONG ◽  
MOHAMED S. KAMEL
Keyword(s):  
Learning Algorithms ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Class Imbalance Problem ◽  
Class Distribution ◽  
Imbalance Problem ◽  
Misclassification Costs ◽  
Imbalanced Class Distribution ◽  
Classifier Learning

Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.

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