Communications of the ACM - Special issue on parallelism
Instance-based prediction of real-valued attributes
Computational Intelligence
Instance-Based Learning Algorithms
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 Review - Special issue on lazy learning
Towards a Unified Theory of Adaption in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Comparing and aggregating rankings with ties
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning label preferences: ranking error versus position error
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
CBR Supports Decision Analysis with Uncertainty
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Context-dependent feedback prioritisation in exploratory learning revisited
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Preference-Based CBR: first steps toward a methodological framework
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Multilayer perceptron for label ranking
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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The problem of label ranking has recently been introduced as an extension of conventional classification in the field of machine learning. In this paper, we argue that label ranking is an amenable task from a CBR point of view and, in particular, is more amenable to supporting case-based problem solving than standard classification. Moreover, by developing a case-based approach to label ranking, we will show that, the other way round, concepts and techniques from CBR are also useful for label ranking. In addition to an experimental study in which case-based label ranking is compared to conventional nearest neighbor classification, we present an application in which label ranking is used for node ordering in heuristic search.