Communications of the ACM - Special issue on parallelism
A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized k-nearest neighbor rules
Fuzzy Sets and Systems - Special issue: Dedicated to the memory of Richard E. Bellman
Training sets and a priori probabilities with the nearest neighbour method of pattern recognition
Pattern Recognition Letters
Instance-based prediction of real-valued attributes
Computational Intelligence
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
A fuzzy extended k-nearest neighbors rule
Fuzzy Sets and Systems
When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Selecting typical instances in instance-based learning
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
Artificial Intelligence
Case-based reasoning
Unifying instance-based and rule-based induction
Machine Learning
What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Intelligent Selection of Instances for Prediction Functions in LazyLearning Algorithms
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Advances in instance-based learning algorithms
Advances in instance-based learning algorithms
Lazy learning
Machine learning from examples: inductive and lazy methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Formalizing case based inference using fuzzy rules
Soft computing in case based reasoning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge-Driven versus Data-Driven Logics
Journal of Logic, Language and Information
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Toward a Probabilistic Formalization of Case-Based Inference
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A New Perspective on Reasoning with Fuzzy Rules
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Fuzzy set-based methods in instance-based reasoning
IEEE Transactions on Fuzzy Systems
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Possibilistic classifiers for uncertain numerical data
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
Naive possibilistic classifiers for imprecise or uncertain numerical data
Fuzzy Sets and Systems
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A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a possibilistic version of the similarity-guided extrapolation principle underlying the instance-based learning paradigm is proposed. This version is compared to the commonly used probabilistic approach from a methodological point of view. Moreover, aspects of knowledge representation such as the modeling of uncertainty are discussed. Taking the possibilistic extrapolation principle as a point of departure, an instance-based learning procedure is outlined which includes the handling of incomplete information, methods for reducing storage requirements and the adaptation of the influence of stored cases according to their typicality. First theoretical and experimental results showing the efficiency of possibilistic instance-based learning are presented as well.