Handbook of Image and Video Processing
Handbook of Image and Video Processing
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature selection based on the Shapley value
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning to classify with missing and corrupted features
Proceedings of the 25th international conference on Machine learning
Generalization from Observed to Unobserved Features by Clustering
The Journal of Machine Learning Research
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Adversarial Pattern Classification Using Multiple Classifiers and Randomisation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Ensemble Based Data Fusion for Gene Function Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
Machine learning in adversarial environments
Machine Learning
Classifier evaluation and attribute selection against active adversaries
Data Mining and Knowledge Discovery
Stackelberg games for adversarial prediction problems
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Understanding the risk factors of learning in adversarial environments
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Multiple classifier systems under attack
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Adversarial support vector machine learning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Boosting with side information
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Static prediction games for adversarial learning problems
The Journal of Machine Learning Research
Security analysis of online centroid anomaly detection
The Journal of Machine Learning Research
A novel variable precision (θ,σ)-fuzzy rough set model based on fuzzy granules
Fuzzy Sets and Systems
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When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier. This is particularly important in domains with nonstationary feature distributions or with input sensor failures. A common approach to achieving such robustness is to introduce regularization which spreads the weight more evenly between the features. However, this strategy is very generic, and cannot induce robustness specifically tailored to the classification task at hand. In this work, we introduce a new algorithm for avoiding single feature over-weighting by analyzing robustness using a game theoretic formalization. We develop classifiers which are optimally resilient to deletion of features in a minimax sense, and show how to construct such classifiers using quadratic programming. We illustrate the applicability of our methods on spam filtering and handwritten digit recognition tasks, where feature deletion is indeed a realistic noise model.