Instance-Based Learning Algorithms
Machine Learning
Case-based reasoning
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
Lazy learning
k-order additive discrete fuzzy measures and their representation
Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
Selective sampling for nearest neighbor classifiers
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Inside Case-Based Reasoning
A General Framework for Induction and a Study of Selective Induction
Machine Learning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
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The case-based learning paradigm relies upon memorizing cases in the form of successful problem solving experience, such as e.g. a pattern along with its classification in pattern recognition or a problem along with a solution in case-based reasoning. When it comes to solving a new problem, each of these cases serves as an individual piece of evidence that gives an indication of the solution to that problem. In this paper, we elaborate on issues concerning the proper combination (aggregation) of such pieces of evidence. Particularly, we argue that cases retrieved from a case library must not be considered as independent information sources, as implicitly done by most case-based learning methods. Focusing on the problem of prediction as a performance task, we propose a new inference principle that combines potentially interacting pieces of evidence by means of the so-called (discrete) Choquet-integral. Our method, called Cho-k-NN, takes interdependencies between the stored cases into account and can be seen as a generalization of weighted nearest neighbor estimation.