Mohd Khalid Awang

Data Mining for Churn Prediction: Multiple Regressions Approach

The rapid development in the telecommunications industry contributed to the increased rivalry among the competitors. Customers switch to competitors or move out from the service provider become critical concerns for companies to retain customer loyalty. Churn prevention through churn prediction is one of the methods to ensure customer loyalty with the service provider. Detect and analyze early churn is a proactive step to ensure that existing customers did not move out or subscribe to the product from competitors. Selection of customer characteristics is one of the core issues to forecast customer churn in the telecommunications industry. This paper proposes multiple regressions analysis to predict the customers churn in the telecommunications industry based on recommended features. The results have shown that the performance of multiple regressions for predicting customer churn is acceptably good.

A New Customer Churn Prediction Approach Based on Soft Set Ensemble Pruning

Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn.

Regional frequency analysis of streamflow based on partial L-moments approach

An attempt has been made to model the annual maximum streamflow in the East Coast of Peninsular Malaysia, utilizing the guidelines in regional flood frequency analysis. The L-moments and partial L-moments (PL-moments) at several censoring levels are employed to estimate the regional parameters of three extreme value distributions, namely, generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA) distributions. A total number of 18 streamflow stations located throughout the eastern region of Peninsular Malaysia are used as case study. The performances of the L-moments and PL-moments methods and their corresponding distribution functions are compared using Monte Carlo simulation. The results of relative root mean square error (RRMSE) and relative bias (RBIAS) show that the GLO distribution of PL-moments at censoring level 0.1 is appropriate to model the regional streamflow data in East Coast of Peninsular Malaysia compared to L-moments. The overall simulation results indicated that, in some situation, the PL-moments method improves the streamflow quantile prediction and provide useful tools for application in regional flood frequency analysis.

Multiple Criteria Preference Relation by Dominance Relations in Soft Set Theory

This paper presents the applicability of soft set theory for discovering the preference relation in multi-valued information systems. The proposed approach is based on the notion of multi-soft sets. An inclusion of objects into value set of decision class in soft set theory is used to discover the relation between objects based on preference relation. Results from the experiment shows that dominance relation based on soft theory for preference relation is able to produce a finer object classification by eliminating inconsistencies during classification process as opposed to the expert judgement classification.