Communications of the ACM - Special issue on parallelism
The use of knowledge in analogy and induction
The use of knowledge in analogy and induction
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Similarity metric learning for a variable-kernel classifier
Neural Computation
Unifying instance-based and rule-based induction
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks for pattern recognition
Neural networks for pattern recognition
Improving accuracy by combining rule-based and case-based reasoning
Artificial Intelligence
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Artificial Intelligence Review - Special issue on lazy learning
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Discovery of Decision Rules by Matching New Objects Against Data Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Covering with Reducts - A Fast Algorithm for Rule Generation
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
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
Local Attribute Value Grouping for Lazy Rule Induction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Combining the Strength of Pattern Frequency and Distance for Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Center-Based Indexing for Nearest Neighbors Search
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Center-based indexing in vector and metric spaces
Fundamenta Informaticae
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Journal of the ACM (JACM)
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
On Granular Rough Computing: Factoring Classifiers Through Granulated Decision Systems
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data
Transactions on Rough Sets IX
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Reducts Evaluation Methods Using Lazy Algorithms
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Two Families of Classification Algorithms
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Neighborhood graphs for indexing and retrieving multi-dimensional data
Journal of Intelligent Information Systems
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
Applications of rough set based K-means, Kohonen SOM, GA clustering
Transactions on rough sets VII
Comparison of lazy classification algorithms based on deterministic and inhibitory decision rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Transactions on rough sets XII
Application of the Method of Editing and Condensing in the Process of Global Decision-making
Fundamenta Informaticae
Knowledge discovery by relation approximation: a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
Multimodal classification: case studies
Transactions on Rough Sets V
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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Analogy-based reasoning methods in machine learning make it possible to reason about properties of objects on the basis of similarities between objects. A specific similarity based method is the k nearest neighbors (k-nn) classification algorithm. In the k-nn algorithm, a decision about a new object x is inferred on the basis of a fixed number k of the objects most similar to x in a given set of examples. The primary contribution of the dissertation is the introduction of two new classification models based on the k-nn algorithm. The first model is a hybrid combination of the k-nn algorithm with rule induction. The proposed combination uses minimal consistent rules defined by local reducts of a set of examples. To make this combination possible the model of minimal consistent rules is generalized to a metric-dependent form. An effective polynomial algorithm implementing the classification model based on minimal consistent rules has been proposed by Bazan. We modify this algorithm in such a way that after addition of the modified algorithm to the k-nn algorithm the increase of the computation time is inconsiderable. For some tested classification problems the combined model was significantly more accurate than the classical k-nn classification algorithm. For many real-life problems it is impossible to induce relevant global mathematical models from available sets of examples. The second model proposed in the dissertation is a method for dealing with such sets based on locally induced metrics. This method adapts the notion of similarity to the properties of a given test object. It makes it possible to select the correct decision in specific fragments of the space of objects. The method with local metrics improved significantly the classification accuracy of methods with global models in the hardest tested problems. The important issues of quality and efficiency of the k-nn based methods are a similarity measure and the performance time in searching for the most similar objects in a given set of examples, respectively. In this dissertation both issues are studied in detail and some significant improvements are proposed for the similarity measures and for the search methods found in the literature.