Author:
Paolo Soda
Affiliation:
Facoltà di Ingegneria, Università Campus Bio-Medico di Roma, Italy
Keyword(s):
Statistical Pattern Recognition, Decomposition Methods, One-per-class, Reliability Estimation, Classifier Ensembles.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Multiclass learning problems can be cast as the task of assigning instances to a finite set of classes. Although in the wide variety of learning tools there exist some algorithms capable of handling polychotomies, many of the tools were designed by nature for dichotomies. In the literature, many techniques that decompose a polychotomy into a series of dichotomies have been proposed. One of the possible approaches, known as one-per-class, is based on a pool of binary modules, where each one distinguishes the elements of one class from those of the others. In this framework, we propose a novel reconstruction criterion, i.e. a rule that sets the final decision on the basis of the single binary classifications. It looks at the quality of the current input and, more specifically, it is a function of the reliability of each classification act provided by the binary modules. The approach has been tested on four biological and medical datasets and the achieved performance has been compared w
ith the one previously reported in the literature, showing that the method improves the accuracies so far.
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