A novel evolutionary data mining algorithm with applications to churn prediction

Abstract

Abstract-Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problem as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.

Relevant Drivers for Customers` Churn and Retention Decision in the Nigerian Mobile Telecommunication Industry

“…The methods which are most frequently used in research, therefore, have been recognised as neural networks, classification trees and regression (Van Den Poel, & Lariviere, 2004;Au, Chan & Yao, 2003;Boone & Roehm, 2002). Therefore, Hadden et al, (2006) pointed out suitability of these technologies for predicting customer churn using the complaints data previously mentioned.…”

Predicting Community Evolution in Social Networks

“…Davis et al [9] tackled the problem of multi-relational link prediction by extending the neighbourhood methods with weight and focusing on triads. Richter et al [10] and Wai-Ho et al [11] faced the very important task of churn prediction. Wai-Ho et al introduced a new data mining algorithm called DMEL (data mining by evolutionary learning), which estimates each prediction being made.…”

Performance Evaluation of Different Data Mining Classification Algorithm and Predictive Analysis

“…Classification [1] is one of the most useful techniques in data mining to build classification models from an input data set. The used classification techniques commonly build models that are used to predict future data trends [2,3]. Model construction: describing a set of predetermined classes 1.…”