Hybrid Particle Swarm Optimization-Extreme Learning Machine Algorithm for Customer Churn Prediction

Li, Koh Guan; Marikannan, Booma Poolan

doi:10.1166/jctn.2019.8304

Cited by 4 publications

(5 citation statements)

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“…By analyzing this diverse dataset, we aim to evaluate the effectiveness of the churn model and its application of machine learning algorithms in predicting customer churn.The following metrics are used to evaluate the performance of the customer churn prediction: precision, recall, f-measure, and accuracy. It evaluates a predictive model's accuracy in predicting when a client will leave [24]. The information obtained using the confusion matrix is used to generate the four measures described above.…”

Section: Results Discussionmentioning

confidence: 99%

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

Sonawane,

Thorat,

Suryawanshi

et al. 2023

IJRITCC

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The identification of customer churn is a major issue for large telecom businesses. In order to manage the data of current customers as well as acquire and manage new customers, every day, a substantial volume of data gets generated. Therefore, it's crucial to identify the causes of client churn so that the appropriate steps can be taken to lower it. Numerous researchers have already discussed their efforts to combine static and dynamic approaches in order to reduce churn in big data sets, but these systems still have many issues when it comes to actually identifying churn. In this paper, we suggested two methods, the first of which is churn identification and using Natural Language Processing (NLP) methods and machine learning techniques, we make predictions based on a vast telecommunication data set. The NLP process involves data pre-processing, normalization, feature extraction, and feature selection. For feature extraction, we employ unique techniques like TF-IDF, Stanford NLP, and occurrence correlation methods, have been suggested. Throughout the lesson, a machine learning classification algorithm is used for training and testing. Finally, the system employs a variety of cross validation techniques and training and evaluating Machine learning algorithms. The experimental analysis shows the system's efficacy and accuracy.

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Section: Results Discussionmentioning

confidence: 99%

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

Sonawane,

Thorat,

Suryawanshi

et al. 2023

IJRITCC

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“…The author used k-means particle swarm optimization for cluster analysis and the C5.0 classification method to formulate classification rules. Swarm optimization was also used by Li and Marikannan (2019). Qiu et al (2020) used the k-means-HF method for silent customer segmentation, to identify a segment of customers that a company is likely to lose.…”

Section: Market Segmentation Using Cluster Analysismentioning

confidence: 99%

Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

Bach

Pivar

Jaković

2021

JRFM

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The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for different market segments using cluster analysis and decision trees. In the first stage, a case study churn dataset is prepared for the analysis, consisting of demographics, usage of telecom services, contracts and billing, monetary value, and churn. In the second stage, k-means cluster analysis is used to identify market segments for which chi-square analysis is applied to detect the clusters with the highest churn ratio. In the third stage, the chi-squared automatic interaction detector (CHAID) decision tree algorithm is used to develop classification models to identify churn determinants at the clusters with the highest churn level. The contribution of this paper resides in the development of the structured approach to churn management using clustering and classification, which was tested on the churn dataset with a rich variable structure. The proposed approach is continuous since the results of market segmentation and rules for churn prediction can be fed back to the customer database to improve the efficacy of churn management.

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“…They achieved an accuracy of 89.08% in churn prediction rate by using feed-forward neural networks. [4] proposed the usage of feed-forward neural network architecture with particle swarm optimisation. Particle swarm optimisation provides weight tuning for the proposed neural network Marikannan, 2019), and this model achieved a better accuracy of 92.90% on their local dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Particle swarm optimisation provides weight tuning for the proposed neural network Marikannan, 2019), and this model achieved a better accuracy of 92.90% on their local dataset. Their [4] local dataset belonged to a telecommunication company in Jordan. [5] proposed a genetic algorithm methodology which used a rough set theory.…”

Section: Related Workmentioning

confidence: 99%

User behaviour analysis and churn prediction in ISP

Turkmen

Bahçevan²,

Alkhanafseh³

et al. 2020

GJPAAS

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There is no doubt that customer retention is vital for the service sector as companies’ revenue is significantly based on their customers’ financial returns. The prediction of customers who are at the risk of leaving a company’s services is not possible without using their connection details, support tickets and network traffic usage data. This paper demonstrates the importance of data mining and its outcome in the telecommunication area. The data in this paper are collected from different sources like Net Flow logs, call records and DNS query logs. These different types of data are aggregated together to decrease the missing information. Finally, machine learning algorithms are evaluated based on the customer dataset. The results of this study indicate that the gradient boosting algorithm performs better than other machine learning algorithms for this dataset. Keywords: Data analysis, customer satisfaction, subscriber churn, machine learning, telecommunication.

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Hybrid Particle Swarm Optimization-Extreme Learning Machine Algorithm for Customer Churn Prediction

Cited by 4 publications

References 0 publications

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

Churn Identification and Prediction from a Large-Scale Telecommunication Dataset Using NLP

Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees

User behaviour analysis and churn prediction in ISP

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