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International Journal of Scientific Development and Research - IJSDR
(An International Open Access Journal)
ISSN:
2455-2631


Issue: November 2021

Volume 6 | Issue 11

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Paper Title: Analysis of Evaluation Metrics for class imbalance problem using supervised learning approches
Authors Name: R Bulli Babu , Dr. Mohammed Ali Hussain
Unique Id: IJSDR1711023
Published In: Volume 2 Issue 11, November-2017
Abstract: The class imbalance problem has been recognized in many practical domains and a hot topic of machine learning in recent years. In such a problem, almost all the examples are labeled as one class, while far fewer examples are labeled as the other class, usually the more important class. In this case, standard machine learning algorithms tend to be overwhelmed by the majority class and ignore the minority class since traditional classifiers seeking an accurate performance over a full range of instances. This paper reviewed academic activities special for the class imbalance problem firstly. Then investigated various remedies in four different levels according to learning phases. Following surveying evaluation metrics and some other related factors, this paper showed some future. In this paper, we present a new hybrid frame work and two algorithms dubbed as Class Imbalance Learning using Intelligent Under Sampling—Tree and Neural Network versions (CILIUS-T, CILIUS-NN) for learning from skewed training data. These algorithms pro- vide a simpler and faster alternative by using C4.5 and Neural Network as base algorithm. We conduct experiments using ten UCI datasets from various application domains using five algorithms for comparison on five evaluation metrics. Expert- mental results show that our method has higher Area under the ROC Curve, F-measure, precision, TP rate and TN rate values than many existing class imbalance learning methods
Keywords: Dataset, class imbalance, Class imbalance •Weighted sampling • CILIUS
Cite Article: "Analysis of Evaluation Metrics for class imbalance problem using supervised learning approches", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.2, Issue 11, page no.123 - 132, November-2017, Available :http://www.ijsdr.org/papers/IJSDR1711023.pdf
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Publication Details: Published Paper ID: IJSDR1711023
Registration ID:170853
Published In: Volume 2 Issue 11, November-2017
DOI (Digital Object Identifier):
Page No: 123 - 132
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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