Journal Article10.1007/S12046-020-01411-4
Simultaneous two-sample learning to address binary class imbalance problem in low-resource scenarios
1
TL;DR: This paper introduces a novel technique to handle the class imbalance problem, even in low-resource scenarios, and instead of, as is common, learning using one sample at a time, two samples are simultaneously considered to train the classifier.
read more
Abstract: Binary class imbalance problem refers to the scenario where the number of training samples in one class is much lower compared with the number of samples in the other class This imbalance hinders the applicability of conventional machine learning algorithms to classify accurately Moreover, many real world training datasets often fall in the category where data is not only imbalanced but also low-resourced In this paper we introduce a novel technique to handle the class imbalance problem, even in low-resource scenarios In our approach, instead of, as is common, learning using one sample at a time, two samples are simultaneously considered to train the classifier The simultaneous two-sample learning seems to help the classifier learn both intra- and inter-class properties Experiments conducted on a large number of benchmarked datasets demonstrate the enhanced performance of our technique over the existing state of the art techniques
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Identification of phishing websites through hyperlink analysis and rule extraction
TL;DR: The proposed approach can elucidate patterns of phishing websites and identify them accurately and outperforms 16 commonly used classifiers in terms of interpretability and identification performance.
References
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Learning from Imbalanced Data
Haibo He,E.A. Garcia +1 more
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
8.2K
Ensemble based systems in decision making
TL;DR: Conditions under which ensemble based systems may be more beneficial than their single classifier counterparts are reviewed, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined are reviewed.
3.1K
•Proceedings Article
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Jesús Alcalá-Fdez,Alberto Fernández,Julián Luengo,Joaquín Derrac,Salvador García,Luciano Sánchez,Francisco Herrera +6 more
- 01 Jan 2011
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.