Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
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
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Data Compression and Local Metrics for Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Importance of Similitude: An Entropy-Based Assessment
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Feature interval learning algorithms for classification
Knowledge-Based Systems
Data clustering with size constraints
Knowledge-Based Systems
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
A novel two-level nearest neighbor classification algorithm using an adaptive distance metric
Knowledge-Based Systems
The development of intuitive knowledge classifier and the modeling of domain dependent data
Knowledge-Based Systems
Knowledge acquisition based on learning of maximal structure fuzzy rules
Knowledge-Based Systems
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In this paper, we have introduced a new method in which every training point learns what is happening in its neighborhood. So, a hyperplane is learned and associated to each point. With this hyperplane we can define the bands distance, a distance measure that bring closer or move away points depending on its classes. We have used this new distance in classification tasks and have performed tests over 68 datasets: 18 well-known UCI-Repository datasets, one private dataset, and 49 ad hoc synthetic datasets. We have used 10-fold cross-validation and, in order to compare the results of the classifiers, we have considered the mean accuracy and have also performed a paired two-tailored t-Student's test with a significance level of 95%. The results are encouraging and confirm the good behavior of the new proposed classification method. The bands distance has obtained the best overall results with 1-NN and k-NN classifiers when compared with other distances. Finally, we extract conclusions and outline some lines of future work.