Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Machine Learning
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Hierarchical multi-label prediction of gene function
Bioinformatics
Ensemblator: An ensemble of classifiers for reliable classification of biological data
Pattern Recognition Letters
Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
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Abstraction is one of the powerful basic techniques for solving complex problems. In this paper we use abstraction along with hierarchical learning to propose a new classification model which is called ''Learning by Abstraction (LA)''. The key idea in LA is to apply both supervised and unsupervised learning algorithms for solving complex classification problems. In addition, the proposed model can be useful in semi-supervised learning problems in which we just know the high level category of some training instances. In the learning mode of the proposed model, we find the nearest classes and merge them into a new abstract class. We call the collection of this new abstract class with other existing classes a new abstract level of learning. Then, a new learner is trained to perform the classification task in this abstract level. In the recall mode, in order to classify a new instance we combine the decision of these classifiers using a new classifier ensemble model based on Dempster-Shafer's theory and Bayesian ensemble model. The simulation study results show that the proposed model has two major advantages. First, it can improve the correct classification rate (CCR) of an ordinary classifier, especially in complex classification tasks with high dimensional feature vector and many target classes. Second, the new model is robust to the noise and the rate of CCR improvement of the proposed model increases as the noise level of data goes up. In addition, the proposed model has been examined on a real data set of protein fold pattern recognition problem in which the correct classification rate of the RBF neural network has been improved by about 10%.