Application of Feature Extraction Method in Customer Churn Prediction Based on Random Forest and Transduction

2010 ◽  
Vol 5 (3) ◽  
pp. 73-78 ◽  
Author(s):  
Qiu Yihui ◽  
Mi Hong
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Extraction Method ◽  
Churn Prediction ◽  
Feature Extraction Method ◽  
Customer Churn ◽  
Customer Churn Prediction

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

scholarly journals Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

2020 ◽  
Vol 10 (2) ◽  
pp. 1406
Author(s):  
Asia Mahdi Naser alzubaidi ◽  
Eman Salih Al-Shamery
Keyword(s):  
Discriminant Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Lift Coefficient ◽  
Telecommunications Industry ◽  
Feature Analysis ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

scholarly journals Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

2020 ◽  
Vol 9 (3) ◽  
pp. 475-482
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Fierce Competition

Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.

scholarly journals Customer churn prediction based on LASSO and Random Forest models

2019 ◽  
Vol 631 ◽  
pp. 052008
Author(s):  
Qiannan Zhu ◽  
Xinyi Yu ◽  
Yuankang Zhao ◽  
Deyi Li
Keyword(s):  
Random Forest ◽  
Churn Prediction ◽  
Customer Churn ◽  
Forest Models ◽  
Random Forest Models ◽  
Customer Churn Prediction

Robust Texture Feature Extraction Method for Geometrical and Illumination Distortions

2009 ◽  
Vol 129 (5) ◽  
pp. 985-992 ◽  
Author(s):  
Norisuke Takao ◽  
Zhuo Liu ◽  
Shigeo Wada
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Feature Extraction Method
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