Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi, Nhlakanipho Michael; Naicker, Nalindren; Adeliyi, Timothy T.

doi:10.1155/2021/7194728

Cited by 33 publications

(19 citation statements)

References 49 publications

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“…As there is a huge difference in ratio of positive to negative classes, state of the art techniques were implemented to address class imbalance. Two strategies namely Synthetic Minority Over-sampling Technique (SMOTE) (44) and Near-Miss (NM) (45, 46) were used to balance the dataset. Packages in python are available for implementation of both techniques (47).…”

Section: Methodsmentioning

confidence: 99%

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

Nath

Bora

2022

Preprint

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Pre-MicroRNAs are the hairpin loops which produces microRNAs that negatively regulate gene expression in several organisms. In insects, microRNAs participate in several biological processes including metamorphosis, reproduction, immune response, etc. Numerous tools have been designed in recent years to predict pre-microRNA using binary machine learning classifiers where predictive models are trained with true and pseudo pre-microRNA hairpin loops. Currently however, there are no existing tool that is exclusively designed for insect pre-microRNA detection. In this experiment we trained machine learning classifiers such as Random Forest, Support Vector Machine, Logistic Regression and k-Nearest Neighbours to predict pre-microRNA hairpin loops in insects while using Synthetic Minority Over-sampling Technique and Near-Miss to handle the class imbalance. The trained model on Support Vector Machine achieved accuracy of 92.19% while the Random Forest attained an accuracy of 80.28% on our validation dataset. These models are hosted online as web application called RNAinsecta. Further, searching target for the predicted pre-microRNA in insect model organism Drosophila melanogaster has been provided in RNAinsecta using miRanda at the backend where experimentally validated genes regulated by microRNA are collected from miRTarBase as target sites. RNAinsecta is freely available at https://rnainsecta.in .

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Section: Methodsmentioning

confidence: 99%

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

Nath

Bora

2022

Preprint

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“…Accuracy measures the proportion of correctly classi ed instances out of the total instances and provides a fundamental indicator of overall model performance. However, when dealing with imbalanced data, where the number of legitimate transactions far outweighs fraudulent ones, accuracy alone may not be the most informative metric (Mqadi et al, 2021). In such cases, precision, recall, and F1-score become more relevant as they take into account false positives and false negatives, which are critical in fraud detection.…”

Section: Comparative Analysis Of Machine Learning Modelsmentioning

confidence: 99%

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

Josyula

2023

Preprint

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Fraud detection in the fintech sector is a critical area of concern as financial transactions increasingly shift to digital platforms. This paper presents a comprehensive analysis of enhancing fraud detection in fintech by combining machine learning techniques, leveraging behavioral analytics, and adopting RegTech solutions. The objective is to develop a holistic approach that strengthens fraud prevention strategies, ensures regulatory compliance, and safeguards the interests of customers and financial institutions. The paper begins with an introduction that sets the context by highlighting the growing importance of fraud detection in the digital financial landscape. It outlines the research objectives, scope, and structure of the paper. Subsequently, the methodology section details the data collection process, the selection and comparative analysis of machine learning models, the integration of behavioral analytics, and the implementation of RegTech solutions. The paper concludes with a summary of findings and contributions, emphasizing the significance of adopting a holistic approach to fraud detection in the fintech industry. It underscores the need for financial institutions to embrace advanced technologies, comply with data privacy regulations, and collaborate within the industry to combat financial crimes effectively.

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“…As there is a huge difference in ratio of positive to negative classes, state of the art techniques were implemented to address class imbalance. Two strategies namely Synthetic Minority Over-sampling Technique (SMOTE) 39 and Near-Miss (NM) 40,41 were used to balance the dataset. Packages in python are available for implementation of both techniques 42 .…”

Section: Handling Class Imbalancementioning

confidence: 99%

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

Nath

Bora

2022

Preprint

View full text Add to dashboard Cite

Pre-MicroRNAs are the hairpin loops which produces microRNAs that negatively regulate gene expression in several organisms. In insects, microRNAs participate in several biological processes including metamorphosis, reproduction, immune response, etc. Numerous tools have been designed in recent years to predict pre-microRNA using binary machine learning classifiers where predictive models are trained with true and pseudo pre-microRNA hairpin loops. Currently, there is no tool that is exclusively designed for insect pre-microRNA detection. In this experiment we trained machine learning classifiers such as Random Forest, Support Vector Machine, Logistic Regression and k-Nearest Neighbours to predict pre-microRNA hairpin loops in insects while using Synthetic Minority Over-sampling Technique and Near-Miss to handle the class imbalance. The trained model on Support Vector Machine achieved accuracy of 92.19% while the Random Forest attained an accuracy of 80.28% on our validation dataset. These models are hosted online as web application called RNAinsecta. Further, searching target for the predicted pre-microRNA in insect model organism Drosophila melanogaster has been provided in RNAinsecta using miRanda at the backend where experimentally validated genes regulated by microRNA are collected from miRTarBase as target sites. RNAinsecta is freely available at https://rnainsecta.in. GitHub: https://github.com/adhiraj141092/RNAinsecta

show abstract

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Cited by 33 publications

References 49 publications

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

Fraud Detection in Fintech Leveraging Machine Learning and Behavioral Analytics

RNAinsecta: A tool for prediction of pre-microRNA in insects using machine learning algorithms

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