Credit card fraud detection using AdaBoost and majority voting

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards

Mapping of Quantitative Trait Loci for Grain Iron and Zinc Concentration in Diploid A Genome Wheat

Micronutrients, especially iron (Fe) and zinc (Zn), are deficient in the diets of people in underdeveloped countries. Biofortification of food crops is the best approach for alleviating the micronutrient deficiencies. Identification of germplasm with high grain Fe and Zn and understanding the genetic basis of their accumulation are the prerequisites for manipulation of these micronutrients. Some wild relatives of wheat were found to have higher grain Fe and Zn concentrations compared with the cultivated bread wheat germplasm. One accession of Triticum boeoticum (pau5088) that had relatively higher grain Fe and Zn was crossed with Triticum monococcum (pau14087), and a recombinant inbred line (RIL) population generated from this cross was grown at 2 locations over 2 years. The grains of the RIL population were evaluated for Fe and Zn concentration using atomic absorption spectrophotometer. The grain Fe and Zn concentrations in the RIL population ranged from 17.8 to 69.7 and 19.9 to 64.2 mg/kg, respectively. A linkage map available for the population was used for mapping quantitative trait loci (QTL) for grain Fe and Zn accumulation. The QTL analysis led to identification of 2 QTL for grain Fe on chromosomes 2A and 7A and 1 QTL for grain Zn on chromosome 7A. The grain Fe QTL were mapped in marker interval Xwmc382-Xbarc124 and Xgwm473-Xbarc29, respectively, each explaining 12.6% and 11.7% of the total phenotypic variation and were designated as QFe.pau-2A and QFe.pau-7A. The QTL for grain Zn, which mapped in marker interval Xcfd31-Xcfa2049, was designated as QZn.pau-7A and explained 18.8% of the total phenotypic variatio

Credit card fraud detection using a hierarchical behavior-knowledge space model

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems