Abstract:
Multiple classifiers for prediction or classification
has gained popularity in recent years. Ensemble Technique
perform best predictions as compared to traditional classifiers.
This has resulted in the experimentation with new ways of
ensemble creation. This paper presents a multiple classifier
system (MCS) that can outperform traditional classifiers.
Experiments are performed on a benchmark Customer Churn
Dataset (available on UCI repository) and a newly created
dataset from a South Asian wireless telecom operator. MCS
achieved accuracies of 97% and 86% on the UCI churn dataset
and private dataset, respectively. MCS as compared to existing
best approaches realized the best results on the private and
public datasets.
