Communications of the ACM
Statistical analysis with missing data
Statistical analysis with missing data
On the learnability of Boolean formulae
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Learning regular sets from queries and counterexamples
Information and Computation
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
ML92 Proceedings of the ninth international workshop on Machine learning
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Learning with restricted focus of attention
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On learning embedded symmetric concepts
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Active Learning Using Arbitrary Binary Valued Queries
Machine Learning
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Learning to reason with a restricted view
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Probably approximately optimal satisficing strategies
Artificial Intelligence
Active learning for vision-based robot grasping
Machine Learning - Special issue on robot learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Journal of the ACM (JACM)
Learning from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Knowing what doesn't matter: exploiting the omission of irrelevant data
Artificial Intelligence - Special issue on relevance
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Machine Learning
Learning to Reason with a Restricted View
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Machine Learning
Machine Learning
Why Experimentation can be better than "Perfect Guidance"
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Clustering Appearances of 3D Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A classification approach to word prediction
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Journal of Artificial Intelligence Research
Learning to reason the non monotonic case
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Context-sensitive program analysis as database queries
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Learning policies for sequential time and cost sensitive classification
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
Active learning with direct query construction
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing classifier utility when there are data acquisition and modeling costs
Data Mining and Knowledge Discovery
Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
Active Feature-Value Acquisition
Management Science
Learning when to stop thinking and do something!
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Missing or absent? A Question in Cost-sensitive Decision Tree
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
VOILA: efficient feature-value acquisition for classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Active cost-sensitive learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Cost-time sensitive decision tree with missing values
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Journal of Systems and Software
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
Cost sensitive classification in data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Cost-sensitive case-based reasoning using a genetic algorithm: Application to medical diagnosis
Artificial Intelligence in Medicine
Goal-oriented sensor selection for intelligent phones: (GOSSIP)
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Metric anomaly detection via asymmetric risk minimization
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Journal of Artificial Intelligence Research
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Any-cost discovery: learning optimal classification rules
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
Dynamic test-sensitive decision trees with multiple cost scales
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Cost-Sensitive decision trees with multiple cost scales
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Cost-sensitive decision tree for uncertain data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Cost-sensitive classification with unconstrained influence diagrams
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Cost-sensitive decision trees applied to medical data
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
New algorithms for budgeted learning
Machine Learning
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
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Most classification algorithms are "passive", in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can--at some cost--obtain the values of some unspecified attributes, before deciding upon a class label. This can be useful, for instance, when deciding whether to gather information relevant to a medical procedure or experiment. The expected utility of using an active classifier depends on both the cost required to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification. This paper analyzes the problem of learning optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework, we show that this task can be achieved efficiently when the active classifier is allowed to perform only (at most) a constant number of tests. We then show that, in more general environments, this task of learning optimal active classifiers is often intractable.