Study on Emotional Intelligence and Quality of Work life on employees in organizations

Abstract

financial statement fraud has been a difficult program for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to proposed an optimized financial fraud detection model combining feature selection and machine learning classification. I used feature selection to reduce the dimensionality following PCA and Xgboost to do it. Principal component analysis (PCA) is a statistical method. By orthogonal transformation, a set of observations of possibility correlated variables converted into a set of linearly independent variables , which called principal component. I used machine learning methods to explore the variables with support. Vector Machine (SVM), Random forest(RF), Decision Tree ( DT), Artificial Neural Network (ANN), and logistic Regression (LR) for PCA and then with Xgboost. The study indicated that random forest outperformed the other four methods. as two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this research.