Credit Card Fraud Detection Using AdaBoost and Majority Voting

Randhawa, Kuldeep Kaur; Loo, Chu Kiong; Seera, Manjeevan; Lim, Chee Peng; Nandi, Asoke K.

doi:10.1109/access.2018.2806420

Cited by 278 publications

(109 citation statements)

References 21 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…The mathematical interpretation of merging two MBs is simply to find their union set. This leads to the identification of the optimal recommendations based on the majority voting rule commonly used in ML [35,36]. Figure 2 summarizes the proposed algorithm for optimizing a set of decision variables with respect to a target variable while matching the set of relevant features in a BN.…”

Section: Proposed Methodsmentioning

confidence: 99%

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

et al. 2020

View full text Add to dashboard Cite

This paper proposes a novel algorithm for optimizing decision variables with respect to an outcome variable of interest in complex problems, such as those arising from big data. The proposed algorithm builds on the notion of Markov blankets in Bayesian networks to alleviate the computational challenges associated with optimization tasks in complex datasets. Through a case study, we apply the algorithm to optimize medication prescriptions for diabetic patients, who have different characteristics, suffer from multiple comorbidities, and take multiple medications concurrently. In particular, we demonstrate how the optimal combination of diabetic medications can be found by examining the comparative effectiveness of the medications among similar patients. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems. The procedure can be applied to any complex Bayesian network with numerous features, multiple decision variables, and a target variable.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Kuldeep Randhawa [2] compaꭇed ensemble machine leaꭇning methods with standaꭇd methods and concluded that hybꭇid models namely majoꭇity voting method achieves good accuꭇacy ꭇates when used with publicly available caꭇd data and ꭇeal-woꭇld cꭇedit caꭇd data fꭇom a financial institution to detect fꭇaudulent tꭇansactions. A total of 12 machine leaꭇning algoꭇithms ꭇanging fꭇom Neuꭇal Netwoꭇks to Deep Leaꭇning Models along with hybꭇid models such as Adaboost and Majoꭇity Voting.…”

Section: Fig 1 Adaboost Optimization Pꭇoposed By Kisangmentioning

confidence: 99%

Prediction of Onset of Diabetes using Adaptive Boosting

2020

IJRTE

View full text Add to dashboard Cite

Abstract: Diabetes is one of the most common diseases, as per the survey in 2015, 30 million people in US are suffering from this disease, i.e about 90-95 percent of the population. If diabetes is untreated at the early stages, high blood glucose in the body leads to various other health problems like: eye problems, stroke, nerve damage, heart disease, stroke etc. Technology has seen an explosive growth in the development and use of Artificial Intelligence in various domains. The increased sophistication and capabilities of these tools are unlocking new possibilities in fields of Medicine, Agriculture, Manufacturing and Automobiles. The goal of this work is to predict the onset of diabetes using Machine Learning namely Adaptive Boosting. Boosting is a technique wherein a series of low accuracy classifiers are combined to create a high accuracy classifier. In many areas the problems are so complicated that simple algorithms such as KNN, Decision Tress are incapable of making predictions. Hybrid algorithms such as Random Forests and Gradient Boosting are gaining popularity due to these reasons are used by multinational companies one example being Netflix. In this work Decision Tree and Support Vector Machine methods has been considered with eight important attributes namely, Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and predicts if a person has diabetes. Multiple models are built using decision tree and support vector machine without Adaptive Boosting and with Boosting technique and the results are compared and evaluated. Result shows that support vector machine gives an improved overall accuracy of 80%.

show abstract

“…Randhawa et al [15] presented the use of machine learning algorithm has been utilized for the detection of the fraud in the credit card by developing a system. The standard models was utilized initially after which the use of hybrid methods is followed in which AdaBoost is present.…”

Section: Literature Reviewmentioning

confidence: 99%

Fraud prediction for credit card using classification method

Monika

Kaur

2018

IJET

View full text Add to dashboard Cite

With the improvement of innovation like credit cards, debit cards, mobile banking, Internet managing an account is the mainstream medium to exchange the cash starting with one record then onto the next. Credit card is picking up fame day by day which expands the online exchange with the expansion in online shopping, online charge payment, insurance premium and different charges so the extortion cases identified with this are likewise expanding and it puts an extraordinary anxiety on the economy, affecting the two clients and budgetary bodies. It costs cash as well as an awesome measure of time to reestablish the damage done. In this paper, we look whether data mining procedures are valuable to estimate and categorize the client's credit risk score (normal/fraud) to beat the future dangers. The reason for existing is to keep the clients from online exchange by utilizing particular Data mining classification methods. The fakes are ascertained by Naïve Bayes method way to deal with break down the exchange is actual or fake. The exploratory outcome demonstrates that our model has great classification accuracy, recall and precision.

show abstract

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Cited by 278 publications

References 21 publications

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

Prediction of Onset of Diabetes using Adaptive Boosting

Fraud prediction for credit card using classification method

Contact Info

Product

Resources

About