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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-based pattern discrimination: A novel technique for feature selection
Pattern Recognition Letters
Rapid and brief communication: FuzzyBagging: A novel ensemble of classifiers
Pattern Recognition
Cancer classification using Rotation Forest
Computers in Biology and Medicine
RotBoost: A technique for combining Rotation Forest and AdaBoost
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Switching class labels to generate classification ensembles
Pattern Recognition
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 12.06 |
In this work a novel technique for building ensembles of classifiers for spectrogram classification is presented. We propose a simple approach for classifying signals from a large database of plant echoes, these echoes are highly complex stochastic signals, anyway their spectrograms contain enough information for extracting a good set of features for training the proposed ensemble of classifiers. The proposed ensemble of classifiers is a novel modified version of a recent feature transform based ensemble method: the Input Decimated Ensemble. In the proposed variant different subsets of randomly extracted training patterns are used to create a set of different Neighborhood Preserving Embedding subspace projections. These feature transformations are applied to the whole dataset and a set of decision trees are trained using these transformed spaces. Finally, the scores of this set of classifiers are combined by sum rule. Experiments carried out on a yet proposed dataset show the superiority of this method with respect to other approaches. The proposed approach outperforms the yet proposed, for the tested dataset, combination of principal component analysis and support vector machine (SVM). Moreover, we show that the fusion between the proposed ensemble and the system based on SVM outperforms both the stand-alone methods.