CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem

• Published in: Pattern analysis and applications : PAA Vol. 14; no. 4; pp. 395 - 413
• Main Authors: Podolak, Igor T., Roman, Adam
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.11.2011; Springer
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Memory organisation. Data processing; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Software; Theoretical Advances; Face recognition; Hierarchical classification; Neural networks; Multiclass classification; Clustering algorithms; Probabilistic approach; Image processing; Cluster; Neural network; Modeling; Unsupervised learning; Supervised learning; Facies; Hierarchical system; Overlay; Artificial intelligence; Multiclass; Multiple classification
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-011-0204-3

This paper describes in full detail a model of a hierarchical classifier (HC). The original classification problem is broken down into several subproblems and a weak classifier is built for each of them. Subproblems consist of examples from a subset of the whole set of output classes. It is essential for this classification framework that the generated subproblems would overlap, i.e. some individual classes could belong to more than one subproblem. This approach allows to reduce the overall risk. Individual classifiers built for the subproblems are weak, i.e. their accuracy is only a little better than the accuracy of a random classifier. The notion of weakness for a multiclass model is extended in this paper. It is more intuitive than approaches proposed so far. In the HC model described, after a single node is trained, its problem is split into several subproblems using a clustering algorithm. It is responsible for selecting classes similarly classified. The main scope of this paper is focused on finding the most appropriate clustering method. Some algorithms are defined and compared. Finally, we compare a whole HC with other machine learning approaches.