Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
On the Consistency of Information Filters for Lazy Learning Algorithms
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
InstanceRank: Bringing order to datasets
Pattern Recognition Letters
Prototype reduction techniques: A comparison among different approaches
Expert Systems with Applications: An International Journal
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
On the use of data filtering techniques for credit risk prediction with instance-based models
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms.