In this paper, we present a cohort-based classification approach to the churn prediction for social on-line games. The original metric is proposed and tested on real data showing a good increase in revenue by churn preventing. The core of the approach contains such components as tree-based ensemble classifiers and threshold optimization by decision boundary.

Ensembles of classifiers are among the strongest classifiers in most data mining applications. Bagging ensembles exploit the instability of base-classifiers by training them on different bootstrap replicates. It has been shown that Bagging instable classifiers, such as decision trees, yield generally good results, whereas bagging stable classifiers, such as k-NN, makes little difference. Howeve...

Abstract Reservoir operation rules, as an important tool to guide reservoir operation, are not only the decision-making reference in period of planning and design, but also one key technologies affecting comprehensive benefits stage management. Therefore, rules extraction method based on decision tree its ensemble model was proposed this paper. The time index, early water level, inflow, outflow...

The work is devoted to studying SARS-CoV-2-associated pneumonia and the investigating of main indicators that lead patients’ mortality. Using good-known parameters are routinely embraced in clinical practice, we obtained new functional dependencies based on an accessible understandable decision tree ML ensemble classifiers models would allow physician determine prognosis a few minutes and, acco...

Random Forest is an Ensemble Supervised Machine Learning technique. Research work in the area of Random Forest aims at either improving accuracy or improving performance. In this paper we are presenting our research towards improvement in learning time of Random Forest by proposing a new approach called Disjoint Partitioning. In this approach, we are using disjoint partitions of training datase...

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The quality of a learning algorithm is characterized by the accuracy, stability and comprehensibility of the models it generates. Though ensembles produce accurate and stable classifiers, they are hard to interpret. In this paper, we propose a meta-learning method for ID3 that makes use of an ensemble for gaining accuracy and stability and yet produces a single comprehensible classifier. The ma...

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are cont...

In this paper, we have developed two ensembles of neural network classifiers in order to recognize actors’ gender from their biological movements. One is the ensemble of modular MLPs (experts), the other is the ensemble of modular MLPs and an inductive decision tree which combines the output of experts. The human movement database consists of 13 males’ and 13 females’ movements, and contains 10...