A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

Salunkhe, Uma R.; Mali, Suresh N.

doi:10.5815/ijisa.2018.05.08

Cited by 16 publications

(15 citation statements)

References 29 publications

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“…Class Balancing is the optional activity of preprocessing stage which aims to resolve the issue of "Imbalance ratio" [26,27], [35] in datasets. We have used Random Over Sampling (ROS), which reduces the imbalance ratio in dataset by duplicating the instances in minority class.…”

Section: Fig 2 Multi-filter Feature Selection Aggregation Methodsmentioning

confidence: 99%

A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Iqbal¹,

Aftab²

2020

IJMECS

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Production of high quality software at lower cost can be possible by detecting defect prone software modules before the testing process. With this approach, less time and resources are required to produce a high quality software as only those modules are thoroughly tested which are predicted as defective. This paper presents a classification framework which uses Multi-Filter feature selection technique and Multi-Layer Perceptron (MLP) to predict defect prone software modules. The proposed framework works in two dimensions: 1) with oversampling technique, 2) without oversampling technique. Oversampling is introduced in the framework to analyze the effect of class imbalance issue on the performance of classification techniques. The framework is implemented by using twelve cleaned NASA MDP datasets and performance is evaluated by using: F-measure, Accuracy, MCC and ROC. According to results the proposed framework with class balancing technique performed well in all of the used datasets.

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Section: Fig 2 Multi-filter Feature Selection Aggregation Methodsmentioning

confidence: 99%

A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Iqbal¹,

Aftab²

2020

IJMECS

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“…(Table 3). To handle the class imbalance issue [27,28] resampling is performed on all the datasets by using the 'Resample package' provided by Weka. We have chosen the resampling with replacement technique in which a random subsample of dataset is produced where each instance in dataset has equal chance of being selected and an instance can be selected multiple times.…”

Section: Methodsmentioning

confidence: 99%

Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm

Alawad¹,

Rahman²

2019

IJMECS

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Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.

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“…For example, Salunkhe and Mali developed a hybrid re-sampling approach named Synthetic Minority Oversampling Technique-Borderline Under-sampling which is the combination of their novel under-sampling technique and Synthetic Minority Oversampling Technique. They aimed to focus on the necessary data of majority class and avoid their removal in order to overcome the limitation of random undersampling [20]. Sumadhi classifier.…”

Section: B Methodsmentioning

confidence: 99%

A Study on Test Variable Selection and Balanced Data for Cervical Cancer Disease

Akyol¹

2018

IJIEEB

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Cancer is a pestilent disease. One of the most important cancer kinds, cervical cancer is a malignant tumor which threats women's life. In this study, the importance of test variables for cervical cancer disease is investigated by utilizing Stability Selection method. Also, Random Under-Sampling and Random Over-Sampling methods are implemented on the dataset. In this context, the learning model is designed by using Random Forest algorithm. The experimental results show that Stability Selection, Random Over-Sampling and Random Forest based model are more successful, approximately 98% accuracy.

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A Hybrid Approach for Class Imbalance Problem in Customer Churn Prediction: A Novel Extension to Under-sampling

Cited by 16 publications

References 29 publications

A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP

Design of (FPID) controller for Automatic Voltage Regulator using Differential Evolution Algorithm

A Study on Test Variable Selection and Balanced Data for Cervical Cancer Disease

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