Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
Ant Clustering Using Ensembles of Partitions
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Using supervised clustering to enhance classifiers
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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We propose a pre-processing step to classification thatapplies a clustering algorithm to the training set to discoverlocal patterns in the attribute or input space. Wedemonstrate how this knowledge can be exploited to enhancethe predictive accuracy of simple classifiers. Our focusis mainly on classifiers characterized by high bias butlow variance (e.g., linear classifiers); these classifiers experiencedifficulty in delineating class boundaries over theinput space when a class distributes in complex ways. Decomposingclasses into clusters makes the new class distributioneasier to approximate and provides a viable way toreduce bias while limiting the growth in variance. Experimentalresults on real-world domains show an advantagein predictive accuracy when clustering is used as a pre-processingstep to classification.