Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majori...

Economic Machine Learning for Fraud Detection

Fraud Detection - Machine learning to the rescue

Combining Data Mining and Machine Learning for Effective Fraud Detection

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specific...

Emotion Detection in Persian Text; A Machine Learning Model

This study aimed to develop a computational model for recognition of emotion in Persian text as a supervised machine learning problem. We considered Pluthchik emotion model as supervised learning criteria and Support Vector Machine (SVM) as baseline classifier. We also used NRC lexicon and contextual features as training data and components of the model. One hundred selected texts including pol...