Artificial Intelligence Review - Special issue on lazy learning
Rapid Concept Learning for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Rapid Concept Learning for Mobile Robots
Autonomous Robots
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Debiasing Training Data for Inductive Expert System Construction
IEEE Transactions on Knowledge and Data Engineering
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
DS '98 Proceedings of the First International Conference on Discovery Science
Possibilistic instance-based learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Cost-Constrained Data Acquisition for Intelligent Data Preparation
IEEE Transactions on Knowledge and Data Engineering
"Missing Is Useful': Missing Values in Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
IEEE Transactions on Knowledge and Data Engineering
Addressing diverse user preferences in SQL-query-result navigation
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
Multi-group support vector machines with measurement costs: A biobjective approach
Discrete Applied Mathematics
Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
Building a cost-constrained decision tree with multiple condition attributes
Information Sciences: an International Journal
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
An empirical study of the noise impact on cost-sensitive learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
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
Qualitative test-cost sensitive classification
Pattern Recognition Letters
Test-cost sensitive classification on data with missing values in the limited time
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Test-cost sensitive classification using greedy algorithm on training data
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Cost sensitive classification in data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Costs-sensitive classification in multistage classifier with fuzzy observations of object features
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
ACE-Cost: acquisition cost efficient classifier by hybrid decision tree with local SVM leaves
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hybrid cost-sensitive decision tree
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Simple test strategies for cost-sensitive decision trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
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
Comparison of cost for zero-one and stage-dependent fuzzy loss function
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A competition strategy to cost-sensitive decision trees
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
The CASH algorithm-cost-sensitive attribute selection using histograms
Information Sciences: an International Journal
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Traditional learning-from-examples methods assume that examples are given beforehand and all features are measured for each example. However, in many robotic domains the number of features that could be measured is very large, the cost of measuring those features is significant, and thus the robot must judiciously select which features it will measure. Finding a proper tradeoff between the accuracy (e.g., number of prediction errors) and efficiency (e.g., cost of measuring features) during learning (prior to convergence) is an important part of the problem. Inspired by such robotic domains, this article considers realistic measurement costs of features in the process of incremental learning of classification knowledge. It proposes a unified framework for learning-from-examples methods that trade off accuracy for efficiency during learning, and analyzes two methods (CS-ID3 and CS-IBL) in detail. Moreover, this article illustrates the application of such a cost-sensitive-learning method to a real robot designed for an approach-recognize task. The resulting robot learns to approach, recognize, and grasp objects on a floor effectively and efficiently. Experimental results show that highly accurate classification procedures can be learned without sacrificing efficiency in the case of both synthetic and real domains.