The complexity of Markov decision processes
Mathematics of Operations Research
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Decision-theoretic active sensing for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Fast inference and learning in large-state-space HMMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
Feature selection under a complexity constraint
IEEE Transactions on Multimedia - Special section on communities and media computing
Probabilistic action planning for active scene modeling in continuous high-dimensional domains
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Designing efficient cascaded classifiers: tradeoff between accuracy and cost
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Qualitative test-cost sensitive classification
Pattern Recognition Letters
Datum-wise classification: a sequential approach to sparsity
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Multiple costs based decision making with back-propagation neural networks
Decision Support Systems
Besting the quiz master: crowdsourcing incremental classification games
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.01 |
There are many sensing challenges for which one must balance the effectiveness of a given measurement with the associated sensing cost. For example, when performing a diagnosis a doctor must balance the cost and benefit of a given test (measurement), and the decision to stop sensing (stop performing tests) must account for the risk to the patient and doctor (malpractice) for a given diagnosis based on observed data. This motivates a cost-sensitive classification problem in which the features (sensing results) are not given a priori; the algorithm determines which features to acquire next, as well as when to stop sensing and make a classification decision based on previous observations (accounting for the costs of various types of errors, as well as the rewards of being correct). We formally define the cost-sensitive classification problem and solve it via a partially observable Markov decision process (POMDP). While the POMDP constitutes an intuitively appealing formulation, the intrinsic properties of classification tasks resist application of it to this problem. We circumvent the difficulties of the POMDP via a myopic approach, with an adaptive stopping criterion linked to the standard POMDP. The myopic algorithm is computationally feasible, easily handles continuous features, and seamlessly avoids repeated actions. Experiments with several benchmark data sets show that the proposed method yields state-of-the-art performance, and importantly our method uses only a small fraction of the features that are generally used in competitive approaches.