Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data Mining and Knowledge Discovery
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Kernels and Distances for Structured Data
Machine Learning
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A Riemannian approach to graph embedding
Pattern Recognition
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Fusion of statistical and structural fingerprint classifiers
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Feature selection for graph-based image classifiers
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A graph matching based approach to fingerprint classification using directional variance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Graph-Based multiple classifier systems a data level fusion approach
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Recursive processing of cyclic graphs
IEEE Transactions on Neural Networks
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Manifold Learning for Multi-classifier Systems via Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A family of novel graph kernels for structural pattern recognition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Multiple classifiers for graph of words embedding
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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Classifier ensembles aim at a more accurate classification than single classifiers. Different approaches to building classifier ensembles have been proposed in the statistical pattern recognition literature. However, in structural pattern recognition, classifier ensembles have been rarely used. In this paper we introduce a general methodology for creating structural classifier ensembles. Our representation formalism is based on graphs and includes strings and trees as special cases. In the proposed approach we make use of graph embedding in real vector spaces by means of prototype selection. Since we use randomized prototype selection, it is possible to generate n different vector sets out of the same underlying graph set. Thus, one can train an individual base classifier for each vector set und combine the results of the classifiers in an appropriate way. We use extended support vector machines for classification and combine them by means of three different methods. In experiments on semi-artificial and real data we show that it is possible to outperform the classification accuracy obtained by single classifier systems in the original graph domain as well as in the embedding vector spaces.