Competing Risks Survival Models for Churn Prediction

Vasilev, I.A.; Pridanova, I.G.; Petrovskiy, M.I.; Mashechkin, I.V.

Информация о цитировании статьи получена из Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 1 апреля 2026 г.

Авторы: Vasilev I.A., Pridanova I.G., Petrovskiy M.I., Mashechkin I.V.
Журнал: Doklady Mathematics
Том: 112
Год издания: 2025
Издательство: Maik Nauka/Interperiodica Publishing
Местоположение издательства: Russian Federation
Первая страница: 233
Последняя страница: 238
DOI: 10.1134/S1064562425700346
Аннотация: Predicting customer churn requires considering multiple reasons for customer leave, including financial difficulties, dissatisfaction with service quality, and switching to competitors. Survival analysis methods allow us to estimate the probability of an event occurring over time, but have limited functionality in the case of competing risks. The paper proposes adapting classical survival analysis methods to competing risks by constructing a set of individual models using the One-vs-Rest scheme, with different approaches for incorporating censored events. The proposed method for aggregating forecasts using a multi-class classification meta-model will simplify the process of making expert decisions. According to the results of an experimental study using Freddie Mac mortgage lending data, the proposed methods surpassed existing solutions in quality and can be applied to mortgage lending problems.
Добавил в систему: Васильев Юлий Алексеевич

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