C4.5: programs for machine learning
C4.5: programs for machine learning
Application of entropy and energy measures of fuzziness to processing of ECG signal
Fuzzy Sets and Systems
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Handbook of Mathematics and Computational Science
The Handbook of Mathematics and Computational Science
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Support Vector Data Description
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Random subspace method for multivariate feature selection
Pattern Recognition Letters
One-class document classification via Neural Networks
Neurocomputing
Online supervised spam filter evaluation
ACM Transactions on Information Systems (TOIS)
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Minimum spanning tree based one-class classifier
Neurocomputing
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Outlier detection using ball descriptions with adjustable metric
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
“Good” and “bad” diversity in majority vote ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection
Data Mining and Knowledge Discovery
Combining diverse one-class classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
IEEE Network: The Magazine of Global Internetworking
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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One-class classification is one of the most challenging topics in contemporary machine learning and not much attention had been paid to the task of creating efficient one-class ensembles. The paper deals with the problem of designing combined recognition system based on the pools of individual one-class classifiers. We propose the new model dedicated to the one-class classification and introduce novel diversity measures dedicated to it. The proposed model of an one class classifier committee may be used for single-class and multi-class classification tasks. The proposed measures and classification models were evaluated on the basis of computer experiments which were carried out on diverse set of benchmark datasets. Their results confirm that introducing diversity measures dedicated to one-class ensembles is a worthwhile research direction and prove that the proposed models are valuable propositions which can outperform the traditional methods for one-class classification.