Variable precision rough set model
Journal of Computer and System Sciences
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Fundamentals of algorithmics
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Hybrid Search of Feature Subsets
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Feature Selection via Set Cover
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
An introduction to variable and feature selection
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Combined SVM-Based Feature Selection and Classification
Machine Learning
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
On the compact computational domain of fuzzy-rough sets
Pattern Recognition Letters
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Feature selection algorithm for data with both nominal and continuous features
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Concept analysis via rough set and AFS algebra
Information Sciences: an International Journal
Majority voting combination of multiple case-based reasoning for financial distress prediction
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
Information Sciences: an International Journal
Order-based decision rules acquisition in continuous-valued decision information systems
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
A novel attribute reduction algorithm of decomposition based on rough sets
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
Approaches to attribute reduction in concept lattices induced by axialities
Knowledge-Based Systems
Two novel feature selection methods based on decomposition and composition
Expert Systems with Applications: An International Journal
Dynamic particle swarm optimization based on neighborhood rough set model
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Similarity-margin based feature selection for symbolic interval data
Pattern Recognition Letters
Neighborhood rough set and SVM based hybrid credit scoring classifier
Expert Systems with Applications: An International Journal
Evolutionary tolerance-based gene selection in gene expression data
Transactions on rough sets XIV
Dependence and algebraic structure of formal contexts
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Knowledge acquisition in inconsistent multi-scale decision systems
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
A parallel method for computing rough set approximations
Information Sciences: an International Journal
An unsupervised feature selection framework based on clustering
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
Graded rough set model based on two universes and its properties
Knowledge-Based Systems
Neighborhood rough sets for dynamic data mining
International Journal of Intelligent Systems
NMGRS: Neighborhood-based multigranulation rough sets
International Journal of Approximate Reasoning
Rough set approximations in incomplete multi-scale information systems
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Statistical cross-language Web content quality assessment
Knowledge-Based Systems
Matroidal structure of rough sets and its characterization to attribute reduction
Knowledge-Based Systems
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
International Journal of Software Science and Computational Intelligence
An accelerator for attribute reduction based on perspective of objects and attributes
Knowledge-Based Systems
Neighborhood rough sets based multi-label classification for automatic image annotation
International Journal of Approximate Reasoning
Feature selection with test cost constraint
International Journal of Approximate Reasoning
Composite rough sets for dynamic data mining
Information Sciences: an International Journal
Multi-label classification by exploiting label correlations
Expert Systems with Applications: An International Journal
Mixed feature selection in incomplete decision table
Knowledge-Based Systems
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Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mainly designed for dealing with numerical or categorical attributes. However, data usually comes with a mixed format in real-world applications. In this paper, we generalize Pawlak's rough set model into @d neighborhood rough set model and k-nearest-neighbor rough set model, where the objects with numerical attributes are granulated with @d neighborhood relations or k-nearest-neighbor relations, while objects with categorical features are granulated with equivalence relations. Then the induced information granules are used to approximate the decision with lower and upper approximations. We compute the lower approximations of decision to measure the significance of attributes. Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm. We compare the proposed algorithm with others in terms of the number of selected features and classification performance. Experiments show the proposed technique is effective.