Pattern Recognition Letters
Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional binary search trees used for associative searching
Communications of the ACM
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Evolving rule-based systems in two medical domains using genetic programming
Artificial Intelligence in Medicine
A local mean-based nonparametric classifier
Pattern Recognition Letters
Particle swarm optimization for pap-smear diagnosis
Expert Systems with Applications: An International Journal
Dynamic k-nearest-neighbor naive bayes with attribute weighted
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Optimising two-stage recognition systems
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Instance cloning local naive bayes
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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The purpose of group-based classification (GBC) is to determine the class label for a set of test samples, utilising the prior knowledge that the samples belong to same, but unknown class. This can be seen as a simplification of the well studied, but computationally complex, non-sequential compound classification problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. The performances of the proposed techniques are then evaluated on both synthetic and real-world data sets and their performance compared with techniques that label test samples individually. The results show that, while no one algorithm clearly outperforms all others on all data sets, the proposed group-based classification techniques have the potential to outperform the individual-based techniques, especially as the (group) size of the test set increases. In addition, it is shown that algorithms that pool information from the whole test set perform better than two-stage approaches that undertake a vote based on the class labels of individual test samples.