Optimization Of C4.5 Algorithm And Its Application In Credit Evaluation Of Individual Loans

In recent years,with the rapid development of the market economy,the development of personal credit business in China has entered a new peak.Personal credit is becoming more and more important in the banking,finance and other industries.As the foundation of social credit,personal credit for its relatively decentralized business loans,group oriented is larger,loan amount is smaller,the low rate of bad loans and other advantages,is widely regarded as high-quality assets,have gradually become the main priority loan business of each credit institutions.Under this background,remarkable importance of personal credit evaluation,it requires the lender to the basic personal information,income and liabilities of information,personal credit information,loan information and so on give a scientific evaluation to determine whether to grant a loan.This is not only directly related to the quality and efficiency of the use of credit funds such as banks,but also may have an impact on social stability.In some developed countries in Europe and America,personal credit evaluation has been in a mature and stable stage,and even formed a complete set of industrial chain,while the construction of personal credit evaluation system in China is still in its infancy.At the same time,with the rapid development of Internet,information technology is increasingly popular in all walks of life,produced a huge amount of data and out of order,and caused great concern in the information technology industry and the whole society for data mining.The main reason is that all pro can be widely used in massive data,and need to be out of order data into useful information and knowledge,so that you can get the information and knowledge are widely used in various applications,such as market analysis,customer retention,scientific research etc..Among them,data classification is an important topic in the field of data mining.At present,there are many kinds of algorithms used in classification,among which the decision tree classification method is widely studied and applied because of its clear theory,easy to understand and easy to classify into classification rules.At present,the decision tree algorithm is the most widely used and effective method in solving all kinds of classification problems.C4.5 algorithm is one of the most typical algorithm in decision tree algorithm,it not only inherits the advantages of CLS and ID3 algorithm,and optimized on the part of the defects in the data mining research field by many researchers widely known.But there are some drawbacks to the C4.5 algorithm.For example,in the decision tree construction process,you need to sort and scan the data sets multiple times,which leads to inefficient and excessive branching problems.In view of this,this paper aims to improve the efficiency and accuracy of C4.5 algorithm and avoid over fitting for the purpose of research.C++ is used as the programming language,and the personal credit evaluation model is applied as the background.Through the optimized C4.5 algorithm,a set of evaluation index system of individual credit decision tree is established,and the borrowing behavior of individual users is synthetically analyzed and predicted.Finally,the operation efficiency and experimental accuracy of C4.5 algorithm are compared and analyzed before and after improvement,and summarize the results of the experiment.