Instance-Based Learning Algorithms
Machine Learning
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
Neural Computation
Similarity metric learning for a variable-kernel classifier
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
No free lunch for early stopping
Neural Computation
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Analysis of new techniques to obtain quality training sets
Pattern Recognition Letters - Special issue: Sibgrapi 2001
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Deleting and Building Sort Out Techniques for Case Base Maintenance
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
A Fuzzy-Rough Approach for Case Base Maintenance
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Rough Sets Reduction Techniques for Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network
Applied Intelligence
Using rough sets to edit training set in k-NN method
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Enhancing Density-Based Data Reduction Using Entropy
Neural Computation
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
Mining competent case bases for case-based reasoning
Artificial Intelligence
Building CBR systems with jcolibri
Science of Computer Programming
Artificial Intelligence Review
An Improved Condensing Algorithm
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Gaining insight through case-based explanation
Journal of Intelligent Information Systems
A Scalable Noise Reduction Technique for Large Case-Based Systems
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Fast Local Support Vector Machines for Large Datasets
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An evaluation of the usefulness of case-based explanation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
On-demand numerosity reduction for object learning
Proceedings of the workshop on Internet of Things and Service Platforms
Profiling instances in noise reduction
Knowledge-Based Systems
| Hi-index | 0.00 |
To some extent the problem of noise reduction in machine learning has been finessed by the development of learning techniques that are noise-tolerant. However, it is difficult to make instance-based learning noise tolerant and noise reduction still plays an important role in k-nearest neighbour classification. There are also other motivations for noise reduction, for instance the elimination of noise may result in simpler models or data cleansing may be an end in itself. In this paper we present a novel approach to noise reduction based on local Support Vector Machines (LSVM) which brings the benefits of maximal margin classifiers to bear on noise reduction. This provides a more robust alternative to the majority rule on which almost all the existing noise reduction techniques are based. Roughly speaking, for each training example an SVM is trained on its neighbourhood and if the SVM classification for the central example disagrees with its actual class there is evidence in favour of removing it from the training set. We provide an empirical evaluation on 15 real datasets showing improved classification accuracy when using training data edited with our method as well as specific experiments regarding the spam filtering application domain. We present a further evaluation on two artificial datasets where we analyse two different types of noise (Gaussian feature noise and mislabelling noise) and the influence of different class densities. The conclusion is that LSVM noise reduction is significantly better than the other analysed algorithms for real datasets and for artificial datasets perturbed by Gaussian noise and in presence of uneven class densities.