The significance of robust fraud detection systems in the banking sector has grown imperative due to the growing prevalence of online transactions. However, the datasets in these particular areas exhibit a greater abu...
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The significance of robust fraud detection systems in the banking sector has grown imperative due to the growing prevalence of online transactions. However, the datasets in these particular areas exhibit a greater abundance and diversity. The large amount and variety of data in these domains necessitate the utilization of synthetic sales data, which is derived from real data, as an innovative approach for studying fraud prevention. This study initially derives importance scores for various features through the utilization of random forests. Subsequently,four features that exhibit the highest correlation with fraudulent transactions are selected for further investigation. The training and prediction processes for both random forests and decisiontree models are then performed. The study compared the performance of random forests and decisiontree models in fraud monitoring using four features. The results indicate that random forests outperform decisiontrees in terms of accuracy, recall, precision, and F1scores, with improvements of 0.68%, 0.62%, 0.68%, and 0.65% respectively. These findings provide a comprehensive analysis of the performance comparison between random forests and decisiontree models in the context of fraud monitoring.
Nowadays, with the expanding of database application, every fields have accumulated huge amounts of data including the College students' activities records. These records are very meticulously reflecting the statu...
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Nowadays, with the expanding of database application, every fields have accumulated huge amounts of data including the College students' activities records. These records are very meticulously reflecting the status of the students' learning and life by analysis their relationships using data mining techniques. The traditional methods of choosing Excellent students and Outstanding Class Leader and Postgraduate Recommendation and Poor students is manual manipulation. But, in this paper, we develop a system which brings in data mining techniques with decision tree algorithm and association rules mining algorithm. Through analyzing the data from college student library records and consumption records and student score and psychological test done by the students, this information system can automatically show the results under data mining algorithm.
Money laundering behavior recognition was a process of knowledge discovery in databases (KDD). Data Mining was an important technique of KDD. In this paper, the characteristics of Chinese foreign exchange money launde...
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Money laundering behavior recognition was a process of knowledge discovery in databases (KDD). Data Mining was an important technique of KDD. In this paper, the characteristics of Chinese foreign exchange money laundering activities, and combine decisiontree approach with financial domain knowledge were analyzed. The suitable money laundering transaction recognition strategy and method were chosen. By making full use of the real transaction data to carry on the experiment, useful rules of money laundering were discovered. Experimental results on SAS demonstrated that our algorithm could be extremely useful in money laundering recognition.
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