Rough sets, rough relations and rough functions
Fundamenta Informaticae - Special issue: rough sets
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
A tolerance rough set approach to clustering web search results
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
Calculi of Approximation Spaces
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Similarity Relation in Classification Problems
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Inhibitory Rules in Data Analysis: A Rough Set Approach
Inhibitory Rules in Data Analysis: A Rough Set Approach
Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts
Transactions on Computational Science V
Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Rule-Based Similarity for Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Roughfication of numeric decision tables: the case study of gene expression data
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Rough - Granular Computing in Knowledge Discovery and Data Mining
Rough - Granular Computing in Knowledge Discovery and Data Mining
RSCTC'2010 discovery challenge: mining DNA microarray data for medical diagnosis and treatment
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Discovering rules-based similarity in microarray data
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Clustering of rough set related documents with use of knowledge from DBpedia
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Pairwise cores in information systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Dominance-Based rough set approach to case-based reasoning
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Ensembles of bireducts: towards robust classification and simple representation
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Unsupervised Similarity Learning from Textual Data
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Semantic clustering of scientific articles using explicit semantic analysis
Transactions on Rough Sets XVI
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Rules-based Similarity (RBS) is a framework in which concepts from rough set theory are used for learning a similarity relation from data. This paper presents an extension of RBS called Dynamic Rules-based Similarity model (DRBS) which is designed to boost the quality of the learned relation in case of highly dimensional data. Rules-based Similarity utilizes a notion of a reduct to construct new features which can be interpreted as important aspects of a similarity in the classification context. Having defined such features it is possible to utilize the idea of Tversky's feature contrast similarity model in order to design an accurate and psychologically plausible similarity relation for a given domain of objects. DRBS tries to incorporate a broader array of aspects of the similarity into the model by constructing many heterogeneous sets of features from multiple decision reducts. To ensure diversity, the reducts are computed on random subsets of objects and attributes. This approach is particularly well-suited for dealing with "few-objects-many-attributes" problem, such as mining of DNA microarray data. The induced similarity relation and the resulting similarity function can be used to perform an accurate classification of previously unseen objects in a case-based fashion. Experiments, whose results are also presented in the paper, show that the proposed model can successfully compete with other state-of-the-art algorithms such as Random Forest or SVM.