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Heuristic algorithm for credit assessment feature selection

A heuristic algorithm and feature selection technology, applied in computing, computing models, data processing applications, etc., can solve problems such as loss correlation and low efficiency of manual screening features

Inactive Publication Date: 2021-05-07
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY +1
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AI Technical Summary

Problems solved by technology

[0003] In view of the above-mentioned technical problems, the main purpose of the present invention is to provide a heuristic algorithm for credit evaluation feature selection to solve the problems of low efficiency and loss of relevance in the traditional manual screening of features in the field of credit risk evaluation

Method used

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  • Heuristic algorithm for credit assessment feature selection
  • Heuristic algorithm for credit assessment feature selection
  • Heuristic algorithm for credit assessment feature selection

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Embodiment Construction

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0032] refer to figure 1 , the present invention provides a kind of heuristic algorithm for feature selection of credit evaluation, comprising steps:

[0033]S1. Initialize N spider populations based on the SSA algorithm, wherein each spider population contains multiple individual spiders a, and each individual spider a corresponds to a feature data in the feature set; the SSA algorithm is a social spider behavior-based The heuristic algorithm is gene...

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Abstract

The invention provides a heuristic algorithm for credit assessment feature selection, and the algorithm generates a random spider population based on an SSA algorithm, calculates an opposite solution of the spider population by employing an OBL strategy, selects an optimal solution to form the number of OBL populations, carries out the algorithm iteration of the randomly generated spider population and the OBL spider population, calculates the fitness value and vibration value of a spider individual, selects an optimal solution individual by using a local search algorithm LSA, enables unselected individuals to enter a next round of iteration, and outputs all optimal solutions selected by the LSA after the iteration is finished to form an optimal solution set. According to the invention, the algorithm in the invention is learned through a machine instead of traditional artificial feature screening, so that the efficiency of feature screening is improved; compared with a general heuristic algorithm, an OBL strategy is added into the algorithm, so that the space coverage rate and the stability of the algorithm are remarkably improved; according to the algorithm, an LSA algorithm architecture suitable for the P2P field is introduced, and the feature screening accuracy and the model matching degree are improved.

Description

technical field [0001] The invention relates to the technical field of credit risk assessment, in particular to a heuristic algorithm for feature selection of credit assessment. Background technique [0002] In the current credit evaluation process, credit analysts will calculate the impact of characteristic information on default by judging and explaining the factors that affect default. By sorting the features with different degrees of influence in descending order, and selecting the required number of features to inject into the model, the purpose of screening features is achieved. However, due to the different measurement content of users, the influence degree of the same feature will often have large fluctuations in different data sets. When there are many irrelevant or redundant indicators among the indicators to be selected, the selection of indicators by the modeling algorithm itself will increase the running time of the algorithm, make the generated model too compl...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06Q30/06G06Q40/02
CPCG06N3/006G06Q30/0609G06Q40/03
Inventor 张在美李一谈刘彦谢国琪
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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