On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

García, Vicente; Sánchez, J. Salvador; Mollineda, Ramón A.

doi:10.1016/j.knosys.2011.06.013

Cited by 297 publications

(153 citation statements)

References 48 publications

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“…Garcia et al [22] applied four resampling algorithms and eight different classifiers on 17 real datasets. Authors' experiment showed that oversampling the minority class outperforms undersampling the majority class when datasets are strongly imbalanced and there are not significant differences for data with a low imbalance.…”

Section: B Oversamplingmentioning

confidence: 99%

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Wosiak

Karbowiak

2017

Annals of Computer Science and Information Systems

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Abstract-The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance. The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance. To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and compared. The experiments were conducted on five datasets -three binary and two multiclass. They comprise several data preprocessing methods applied on data and the classification with different techniques. The study shows that for some datasets there exists a combination of a certain preprocessing method and a classification technique which outperforms other approaches. For datasets with complex distribution or too many features the ratio of correctly predicted labels may be low regardless what resampling method and classification technique has been applied.

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Section: B Oversamplingmentioning

confidence: 99%

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Wosiak

Karbowiak

2017

Annals of Computer Science and Information Systems

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“…Chris Seiffertet al [13] have examined a new hybrid sampling/boosting algorithm, called RUS-Boost from its individual component AdaBoost and SMOTE-Boost, which is another algorithm that combines boosting and data sampling for learning from skewed training data. V. Garcia et al [14] have investigated the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning is affected when different resampling algorithms transform the originally imbalanced data into artificially balanced class distributions.…”

Section: Literature Survey On Imbalance Datasetsmentioning

confidence: 99%

A Novel Class Imbalance Learning using Ordering Points Clustering

Rao¹,

Rao²,

Lakshmi³

2012

IJCA

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In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples of the other class(es).This paper proposes a method belonging to under sampling approach which uses OPTICS one of the best visualization clustering technique for handling class imbalance problem. In the proposed approach, further Classification of new data is performed by applying C4.5 algorithm as the base algorithm. The method is optimized by the selection of the most suitable clusters for deletion of the majority dataset based on visualization algorithms. An experimental analysis is carried out over a wide range of highly imbalanced data sets and uses the statistical tests suggested in the specialized literature. The results obtained show that our novel proposal outperforms other classic and recent models in terms of Area under the ROC Curve, F-measure, precision, TP rate and TN rate.

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“…Fundamentals to employ ML for effective and improved SFP are consideration of different software metrics [4] - [7], [11] - [14], [29,30], Feature Selection (FS) [1,8,9], [15] - [17], [25], [28,34] and Data Balancing (DB) [9,18], [35] - [38]. For SFP, many software metrics have been proposed but we favor to separate the studies according to the most frequently used metrics: Chidamber and Kemerer's (CK) Object Oriented Metrics (OOM) and McCabe and Halstead Static Code Metrics (SCM) [11].…”

Section: Introductionmentioning

confidence: 99%

“…The other is to re-sample the original dataset, either by oversampling the minority class and/or under-sampling the majority class [18,35,36]. As shown in different studies [35] - [38], balancing data using Synthetic Minority Oversampling Techniques (SMOTE) gives better classification performance. Therefore, this study explores the algorithm of SMOTE for building SFP models with datasets that suffer from class imbalance.…”

Section: Introductionmentioning

confidence: 99%

A Combined-Learning Based Framework for Improved Software Fault Prediction

Yohannese¹,

Li²

2017

IJCIS

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Software Fault Prediction (SFP) is found to be vital to predict the fault-proneness of software modules, which allows software engineers to focus development activities on fault-prone modules, thereby prioritize and optimize tests, improve software quality and make better use of resources. In this regard, machine learning has been successfully applied to solve classification problems for SFP. Nevertheless, the presence of different software metrics, the redundant and irrelevant features and the imbalanced nature of software datasets have created more and more challenges for the classification problems. Therefore, the objective of this study is to independently examine software metrics with multiple Feature Selection (FS) combined with Data Balancing (DB) using Synthetic Minority Oversampling Techniques for improving classification performance. Accordingly, a new framework that efficiently handles those challenges in a combined form on both Object Oriented Metrics (OOM) and Static Code Metrics (SCM) datasets is proposed. The experimental results confirm that the prediction performance could be compromised without suitable Feature Selection Techniques (FST). To mitigate that, data must be balanced. Thus our combined technique assures the robust performance. Furthermore, a combination of Random Forts (RF) with Information Gain (IG) FS yields the highest Receiver Operating Characteristic (ROC) curve (0.993) value, which is found to be the best combination when SCM are used, whereas the combination of RF with Correlation-based Feature Selection (CFS) guarantees the highest ROC (0.909) value, which is found to be the best choice when OOM are used. Therefore, as shown in this study, software metrics used to predict the fault proneness of the software modules must be carefully examined and suitable FST for software metrics must be cautiously selected. Moreover, DB must be applied in order to obtain robust performance. In addition to that, dealing with the challenges mentioned above, the proposed framework ensures the remarkable classification performance and lays the pathway to quality assurance of software.

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On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

Cited by 297 publications

References 48 publications

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

A Novel Class Imbalance Learning using Ordering Points Clustering

A Combined-Learning Based Framework for Improved Software Fault Prediction

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