A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Clustering Algorithms
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Improvement of reliability in banknote classification using reject option and local PCA
Information Sciences—Informatics and Computer Science: An International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
Hierarchical clustering of mixed data based on distance hierarchy
Information Sciences: an International Journal
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning Nondeterministic Classifiers
The Journal of Machine Learning Research
A semi-dependent decomposition approach to learn hierarchical classifiers
Pattern Recognition
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
Information Sciences: an International Journal
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
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In most cases, the main goal of machine learning and data mining applications is to obtain good classifiers. However, final users, for instance researchers in other fields, sometimes prefer to infer new knowledge about their domain that may be useful to confirm or reject their hypotheses. This paper presents a learning method that works along these lines, in addition to reporting three interesting applications in the field of population genetics in which the aim is to discover relationships between species or breeds according to their genotypes. The proposed method has two steps: first it builds a hierarchical clustering of the set of classes and then a hierarchical classifier is learned. Both models can be analyzed by experts to extract useful information about their domain. In addition, we propose a new method for learning the hierarchical classifier. By means of a voting scheme employing pairwise binary models constrained by the hierarchical structure, the proposed classifier is computationally more efficient than previous approaches while improving on their performance.