A customer loss prediction model based on a combined classifier

A predictive model and classifier technology, applied to instruments, character and pattern recognition, data processing applications, etc., can solve the problem of telecommunications customer churn prediction performance is not ideal, and achieve good hit rate and accuracy

Inactive Publication Date: 2019-06-14
CENT SOUTH UNIV
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a combined classification model for the problem that the predictive performance of telecommunication customer churn is not ideal for a single classification algorithm model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A customer loss prediction model based on a combined classifier
  • A customer loss prediction model based on a combined classifier
  • A customer loss prediction model based on a combined classifier

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0017] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the present invention will be further described in detail in the order of the basic principle, macro flow and specific steps in combination with the basic theory and formula drawings below.

[0018] Step 1. Preprocess the sample set.

[0019] There are many methods of data preprocessing: data cleaning, data integration, data transformation, data reduction, etc.

[0020] Data cleaning cleans data by filling in missing values, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies. Realize format standardization, abnormal data removal, error correction, and duplicate data removal. Data integration combines data from multiple data sources and stores them in a unified manner to establish a data warehouse or data mart. Data transformation converts data into a form suitable for data mining through smooth aggregation, data general...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides construction of a loss prediction model combining an echo state network and a support vector machine. Aiming at a non-ideal single model prediction effect, a classifier construction method is explored and improved, and the construction of the classifier can be divided into three aspects of a training set construction strategy, a classifier selection strategy and a predictionvalue determination strategy, comprehensively evaluating features and categories by fusing the Mahalanobis distance and the maximum information coefficient, and jointly measuring the redundancy between the features and the correlation degree between the features and the categories. The advantages of an echo state network and a support vector machine are integrated, and the problem of secondary optimization is solved through linear constraint, so that the performance of a telecom customer loss system is improved, and the prediction effect of a customer loss prediction model is improved.

Description

technical field [0001] The invention relates to the field of telecommunication customer churn classification, in particular to a customer churn prediction model based on a combined classifier. Background technique [0002] Under the current telecommunication market environment, attracting new customers and retaining existing customers have become two important themes of the customer management system of telecommunication operators. Predictive analysis of potential lost customers is a key link in the implementation of customer retention strategies, and has become a hot topic that is widely concerned in the academic and business circles. The purpose of customer churn prediction analysis is to predict customers with a high churn rate and correctly lock potential churn customer groups. On this basis, allocate and adjust limited marketing resources, formulate targeted customer retention strategies, improve the effectiveness of customer retention strategies, increase the return o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/30G06K9/62
Inventor 曾婷凤刘莉平
Owner CENT SOUTH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap