A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Variable Consistency Model of Dominance-Based Rough Sets Approach
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Approximations and Rough Sets Based on Tolerances
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Granular Computing on Binary Relations
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Attribute Core of Decision Table
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Fundamenta Informaticae
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On the coverings by tolerance classes
Information Sciences—Informatics and Computer Science: An International Journal
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
A set theory for rough sets: toward a formal calculus of vague statements
Fundamenta Informaticae - Special issue on theory and applications of soft computing (TASC04)
Topological approaches to covering rough sets
Information Sciences: an International Journal
Entropies and Co-Entropies of Coverings with Application to Incomplete Information Systems
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Basic Concepts in Covering-Based Rough Sets
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Generalized rough sets over fuzzy lattices
Information Sciences: an International Journal
Information Sciences: an International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Cost-Sensitive Decision Trees with Pre-pruning
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Journal of Artificial Intelligence Research
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
Information Sciences: an International Journal
Textural approach to generalized rough sets based on relations
Information Sciences: an International Journal
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Granular computing: structures, representations, and applications
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
Accumulated cost based test-cost-sensitive attribute reduction
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Rule learning for classification based on neighborhood covering reduction
Information Sciences: an International Journal
Optimal sub-reducts with test cost constraint
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
Uncertain data mining: an example in clustering location data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
A comparative study of rough sets for hybrid data
Information Sciences: an International Journal
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Minimum cost attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Quantitative analysis for covering-based rough sets through the upper approximation number
Information Sciences: an International Journal
Using one axiom to characterize rough set and fuzzy rough set approximations
Information Sciences: an International Journal
Entropy measures and granularity measures for set-valued information systems
Information Sciences: an International Journal
Rough set approach to incomplete numerical data
Information Sciences: an International Journal
FRPS: A Fuzzy Rough Prototype Selection method
Pattern Recognition
An extension to rough c-means clustering algorithm based on boundary area elements discrimination
Transactions on Rough Sets XVI
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
On an optimization representation of decision-theoretic rough set model
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Nullity-based matroid of rough sets and its application to attribute reduction
Information Sciences: an International Journal
Characteristic matrix of covering and its application to Boolean matrix decomposition
Information Sciences: an International Journal
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In data mining applications, we have a number of measurement methods to obtain a data item with different test costs and different error ranges. Test costs refer to time, money, or other resources spent in obtaining data items related to some object; observational errors correspond to differences in measured and true value of a data item. In supervised learning, we need to decide which data items to obtain and which measurement methods to employ, so as to minimize the total test cost and help in constructing classifiers. This paper studies this problem in four steps. First, data models are built to address error ranges and test costs. Second, error-range-based covering rough set is constructed to define lower and upper approximations, positive regions, and relative reducts. A closely related theory deals with neighborhood rough set, which has been successfully applied to heterogeneous attribute reduction. The major difference between the two theories is the definition of neighborhood. Third, the minimal test cost attribute reduction problem is redefined in the new theory. Fourth, both backtrack and heuristic algorithms are proposed to deal with the new problem. The algorithms are tested on ten UCI (University of California - Irvine) datasets. Experimental results show that the backtrack algorithm is efficient on rational-sized datasets, the weighting mechanism for the heuristic information is effective, and the competition approach can improve the quality of the result significantly. This study suggests new research trends concerning attribute reduction and covering rough set.