Churn prediction on huge telecom data using hybrid firefly based classification

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ID: 104888
2017
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Abstract
Churn prediction in telecom has become a major requirement due to the increase in the number of telecom providers. However due to the hugeness, sparsity and imbalanced nature of the data, churn prediction in telecom has always been a complex task. This paper presents a metaheuristic based churn prediction technique that performs churn prediction on huge telecom data. A hybridized form of Firefly algorithm is used as the classifier. It has been identified that the compute intensive component of the Firefly algorithm is the comparison block, where every firefly is compared with every other firefly to identify the one with the highest light intensity. This component is replaced by Simulated Annealing and the classification process is carried out. Experiments were conducted on the Orange dataset. It was observed that Firefly algorithm works best on churn data and the hybridized Firefly algorithm provides effective and faster results.
Reference Key
ahmed2017churnegyptian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ahmed, Ammar A.Q.;Maheswari, D.;
Journal egyptian informatics journal
Year 2017
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S1110866517300403
Keywords
chemistry Biology (General) Engineering (General). Civil engineering (General) Technology Science (General) Medical technology physics telecommunication electronic computers. computer science

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