Ying Huang
University College Dublin
Customer retentionAlgorithmMachine learningSupport vector machineData miningRule-based systemLogistic regressionArtificial intelligenceHeuristicSet (abstract data type)Pattern recognitionKey (cryptography)k-means clusteringPrincipal component analysisRule inductionChurn rateFuzzy ruleInterpretabilityFuzzy association rulesDiscretization algorithmAssessment methodsTelecommunications serviceField (computer science)Computer scienceNaive Bayes classifierFuzzy logicCluster analysisTelecommunicationsDiscriminantRegressionDecision treeClassifier (UML)Customer relationship management
5Publications
3H-index
65Citations
Publications 5
Newest
Jul 18, 2016 in ICDM (Industrial Conference on Data Mining)
#1Bingquan Huang (UCD: University College Dublin)
#2Ying Huang (UCD: University College Dublin)H-Index: 3
Last. Mohand Tahar Kechadi (UCD: University College Dublin)H-Index: 5
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Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rule-based learning algorithms designed with the assumption of well-balanced datasets, may prov...
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#1Ying Huang (UCD: University College Dublin)H-Index: 3
#2M. Tahar Kechadi (UCD: University College Dublin)H-Index: 8
Abstract Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. T...
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May 24, 2011 in KDD (Knowledge Discovery and Data Mining)
#1Ying Huang (UCD: University College Dublin)H-Index: 3
#2Bingquan Huang (UCD: University College Dublin)
Last. M.-T. Kechadi (UCD: University College Dublin)H-Index: 1
view all 3 authors...
Rule-based classification methods, which provide the interpretation of a classification, are very useful in churn prediction. However, most of the rule-based methods are not able to provide the prediction probability which is helpful for evaluating customers. This paper proposes a rule induction based classification algorithm, called CRL. CRL applies several heuristic methods to learn a set of rules, and then uses them to predict the customer potential behaviours. The experiments were carried ou...
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Nov 19, 2010 in ADMA (Advanced Data Mining and Applications)
#1T. Sato (UCD: University College Dublin)H-Index: 1
#2B. Q. Huang (UCD: University College Dublin)H-Index: 5
Last. B. BuckleyH-Index: 1
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Failure to identify potential churners affects significantly a company revenues and services that can provide. Imbalance distribution of instances between churners and non-churners and the size of customer dataset are the concerns when building a churn prediction model. This paper presents a local PCA classifier approach to avoid these problems by comparing eigenvalues of the best principal component. The experiments were carried out on a large real-world Telecommunication dataset and assessed o...
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Nov 19, 2010 in ADMA (Advanced Data Mining and Applications)
#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#2T. Satoh (UCD: University College Dublin)H-Index: 1
Last. B. Buckley (UCD: University College Dublin)H-Index: 2
view all 5 authors...
Imbalance distribution of samples between churners and nonchurners can hugely affect churn prediction results in telecommunication services field. One method to solve this is over-sampling approach by PCA regression. However, PCA regression may not generate good churn samples if a dataset is nonlinear discriminant. We employed Genetic K-means Algorithm to cluster a dataset to find locally optimum small dataset to overcome the problem. The experiments were carried out on a real-world telecommunic...
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