Pages that link to "Item:Q2253363"
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The following pages link to A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data (Q2253363):
Displaying 21 items.
- A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (Q138141) (← links)
- A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings (Q297123) (← links)
- A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations (Q323495) (← links)
- Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system (Q505247) (← links)
- Profit driven decision trees for churn prediction (Q2178124) (← links)
- Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (Q2184072) (← links)
- How training on multiple time slices improves performance in churn prediction (Q2239912) (← links)
- Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series (Q2281719) (← links)
- Profit-based churn prediction based on minimax probability machines (Q2301965) (← links)
- Proximal gradient method for huberized support vector machine (Q2337512) (← links)
- Time series interpolation via global optimization of moments fitting (Q2355921) (← links)
- Assessing the impact of derived behavior information on customer attrition in the financial service industry (Q2356275) (← links)
- A diffusion model for churn prediction based on sociometric theory (Q2418404) (← links)
- Behavior-aware user response modeling in social media: learning from diverse heterogeneous data (Q2629684) (← links)
- Soft Quadratic Surface Support Vector Machine for Binary Classification (Q2956867) (← links)
- Three Categories Customer Churn Prediction Based on the Adjusted Real Adaboost (Q3102903) (← links)
- Predicting credit card customer churn using support vector machine based on Bayesian optimization (Q5083788) (← links)
- A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games (Q5130595) (← links)
- Advances in Neural Networks – ISNN 2005 (Q5707244) (← links)
- Explaining and predicting customer churn by monotonic rules induced from ordinal data (Q6572882) (← links)
- Exploiting time-varying RFM measures for customer churn prediction with deep neural networks (Q6589106) (← links)
