New Direct Marketing: How to Implement a Profit-Driven Database Marketing Strategy
New Direct Marketing: How to Implement a Profit-Driven Database Marketing Strategy
Bayesian Inference of Noise Levels in Regression
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Algorithm Design
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A scalable framework for discovering coherent co-clusters in noisy data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Estimating predictive variances with kernel ridge regression
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
SCOAL: A framework for simultaneous co-clustering and learning from complex data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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In several applications involving regression or classification, along with making predictions it is important to assess how accurate or reliable individual predictions are. This is particularly important in cases where due to finite resources or domain requirements, one wants to make decisions based only on the most reliable rather than on the entire set of predictions. This paper introduces novel and effective ways of ranking predictions by their accuracy for problems involving large-scale, heterogeneous data with a dyadic structure, i.e., where the independent variables can be naturally decomposed into three groups associated with two sets of elements and their combination. These approaches are based on modeling the data by a collection of localized models learnt while simultaneously partitioning (co-clustering) the data. For regression this leads to the concept of "certainty lift". We also develop a robust predictive modeling technique that identifies and models only the most coherent regions of the data to give high predictive accuracy on the selected subset of response values. Extensive experimentation on real life datasets highlights the utility of our proposed approaches.