Churn classification model for local telecommunication company based on rough set theory

Makhtar, Mokhairi; Nafis, S.; Mohamed, Mohamad Afendee; Awang, Mohd Khalid; Rahman, Mohd Nordin Abdul; Deris, Mustafa Mat

doi:10.4314/jfas.v9i6s.64

Cited by 19 publications

(13 citation statements)

References 14 publications

(15 reference statements)

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“…Since, the last several years, aspect extraction is becoming a widely studied area. Multiple approaches were used including supervised as well as unsupervised [7]. Several studies showed that the double propagation approach based on syntactic dependency [8] performs well as compared to many supervised learning techniques [9].…”

Section: Figure 1: Classification Of Sentiment Analysismentioning

confidence: 99%

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

Hussain¹,

Rana²,

Iftikhar³

et al. 2022

Computers, Materials &Amp; Continua

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In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user's behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

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Section: Figure 1: Classification Of Sentiment Analysismentioning

confidence: 99%

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

Hussain¹,

Rana²,

Iftikhar³

et al. 2022

Computers, Materials &Amp; Continua

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“…Makhtar et al [16] proposed a model for churn prediction using rough set theory in telecom. As mentioned in this paper Rough Set classification algorithm outperformed the other algorithms like Linear Regression, Decision Tree, and Voted Perception Neural Network.…”

Section: Related Workmentioning

confidence: 99%

Customer churn prediction in telecom using machine learning in big data platform

2019

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IntroductionThe telecommunications sector has become one of the main industries in developed countries. The technical progress and the increasing number of operators raised the level of competition [1]. Companies are working hard to survive in this competitive market depending on multiple strategies. Three main strategies have been proposed to generate more revenues [2]: (1) acquire new customers, (2) upsell the existing customers, and (3) increase the retention period of customers. However, comparing these strategies taking the value of return on investment (RoI) of each into account has shown that the third strategy is the most profitable strategy [2], proves that retaining an existing customer costs much lower than acquiring a new one [3], in addition to being considered much easier than the upselling strategy [4]. To apply the Abstract Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.

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“…Random Forest (RF) method is applied and estimated with the help of AUC. Makhtar et al [14] projected an approach for churn prediction under the employment of rough set theory in telecom services. The Rough Set classification approach has surpassed the previous models like Linear Regression, DT, and Voted Perception NN.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

Pustokhina

Pustokhin

Nguyen

et al. 2021

Complex Intell. Syst.

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Customer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

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Churn classification model for local telecommunication company based on rough set theory

Cited by 19 publications

References 14 publications

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

Customer churn prediction in telecom using machine learning in big data platform

Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

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