Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Boundary methods for distribution analysis
Intelligent methods in signal processing and communications
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming based pattern classification with feature space partitioning
Information Sciences: an International Journal
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector Quantization Technique for Nonparametric Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring Classification Complexity of Image Databases: A Novel Approach
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Using boundary methods for estimating class separability
Using boundary methods for estimating class separability
Multiresolution Estimates of Classification Complexity
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class discriminality measure based on feature space partitioning
Pattern Recognition
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Multiresolution estimates of classification complexity estimate the relative ease with which multivariate data belonging to multiple classes can be separated by non-linear boundaries in high dimensional spaces. In this paper we propose the concept of using multiple classifiers in feature subspaces that are generated by feature space partitioning. We find that the advantage gained by training multiple classifiers for a given data set is far greater than the disadvantage of having less number of samples in each feature subspace to train them. In this paper we take a number of data sets from the UCI repository and show the classification advantage gained by using multiple subspace classifiers in parallel. We also demonstrate that the multi-resolution estimates of classification complexity correlate well with this classification performance averaged across all subspaces.