Research on Machine Learning Methods for Intelligent Decision Problems

In order to improve the accuracy, objectivity and scientificity of decision-making, people propose to use machine learning to automatically learn data and extract complex patterns from it, so as to make objective and fair decisions. Machine learning provides a new way to effectively solve decision support problems in many fields by virtue of its powerful ability to automatically learn data and extract complex patterns from them to make intelligent decisions. This paper mainly studies the construction of a supervisory classification model with strong generalization ability to solve complex decision-making problems such as disease diagnosis and financial risk prediction. Supervised classification learning methods such as SVM and FKNN are mainly studied. Diagnostic decision support methods based on combination RS and SVM, combined LFDA and SVM are proposed.