Classification and Prediction on Rural Property Mortgage Data with Three Data Mining Methods  

作　　者：Kaixi Zhang Yingpeng Hu Yanghui Wu 

机构地区：[1]School of Science, Northwest A&F University, Yangling, China

出　　处：《Journal of Software Engineering and Applications》2018年第7期348-361,共14页软件工程与应用（英文）

摘　　要：The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans by farmers and the strict risk control by the financial institutions. The rural finance corporations should use scientific analysis and investigation of the potential households for overall evaluation of the customers. These include historical credit rating, present family situation, and other related information. Three different data mining methods were applied in this paper to the specifically-collected household data. The objective was to study which factor could be the most important in determining loan demand for households, and in the meanwhile, to classify and predict the possibility of loan demand for the potential customers. The results obtained from the three methods indicated the similar outputs, income level, land area, the way of loan, and the understanding of policy were four main factors which decided the probability of one specific farmer applying for a credit loan. The results also embodied the difference within the three methods for classifying and predicting the loan anticipation for the testing households. The artificial neural network model had the highest accuracy of 91.4 which is better than the other two methods.The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans by farmers and the strict risk control by the financial institutions. The rural finance corporations should use scientific analysis and investigation of the potential households for overall evaluation of the customers. These include historical credit rating, present family situation, and other related information. Three different data mining methods were applied in this paper to the specifically-collected household data. The objective was to study which factor could be the most important in determining loan demand for households, and in the meanwhile, to classify and predict the possibility of loan demand for the potential customers. The results obtained from the three methods indicated the similar outputs, income level, land area, the way of loan, and the understanding of policy were four main factors which decided the probability of one specific farmer applying for a credit loan. The results also embodied the difference within the three methods for classifying and predicting the loan anticipation for the testing households. The artificial neural network model had the highest accuracy of 91.4 which is better than the other two methods.

关 键 词：RURAL Property MORTGAGE BAYESIAN NETWORK Artificial NEURAL NETWORK LOGISTIC Regression Classification and Prediction 

分 类 号：R73[医药卫生—肿瘤]