Neural networks and the bias/variance dilemma
Neural Computation
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Magical thinking in data mining: lessons from CoIL challenge 2000
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Variance and Bias for General Loss Functions
Machine Learning
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Editorial: Data Mining Lessons Learned
Machine Learning
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
On Feature Selection, Bias-Variance, and Bagging
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Learning Bayesian network equivalence classes with Ant Colony optimization
Journal of Artificial Intelligence Research
Stop wasting time: on predicting the success or failure of learning for industrial applications
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Using OVA modeling to improve classification performance for large datasets
Expert Systems with Applications: An International Journal
A comprehensive benchmark of the artificial immune recognition system (AIRS)
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Feature selection for optimizing traffic classification
Computer Communications
Mass scale modeling and simulation of the air-interface load in 3g radio access networks
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
An empirical study of learning from imbalanced data
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
Feature selection for detection of peer-to-peer botnet traffic
Proceedings of the 6th ACM India Computing Convention
P2P traffic classification using ensemble learning
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
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The CoIL Challenge 2000 data mining competition attracted a wide variety of solutions, both in terms of approaches and performance. The goal of the competition was to predict who would be interested in buying a specific insurance product and to explain why people would buy. Unlike in most other competitions, the majority of participants provided a report describing the path to their solution. In this article we use the framework of bias-variance decomposition of error to analyze what caused the wide range of prediction performance. We characterize the challenge problem to make it comparable to other problems and evaluate why certain methods work or not. We also include an evaluation of the submitted explanations by a marketing expert. We find that variance is the key component of error for this problem. Participants use various strategies in data preparation and model development that reduce variance error, such as feature selection and the use of simple, robust and low variance learners like Naive Bayes. Adding constructed features, modeling with complex, weak bias learners and extensive fine tuning by the participants often increase the variance error.