87,430 research outputs found
Statistical and neural classifiers: an integrated approach to design (Advances in Pattern Recognition Series)
No full text
Classifier selection approaches for multi-label problems
No full textWhile it is known that multiple classifier systems can be effective also in multi-label problems, only the classifier fusion approach has been considered so far. In this paper we focus on the classifier selection approach instead. We propose an implementation of this approach specific to multi-label classifiers, based on selecting the outputs of a possibly different subset of multi-label classifiers for each class. We then derive static selection criteria for the macro- and micro-averaged F measure, which is widely used in multi-label problems. Preliminary experimental results show that the considered selection strategy can exploit the complementarity of an ensemble of multi-label classifiers more effectively than selection approaches analogous to the ones used in single-label problems, which select the outputs of the same classifier subset for all classes. Our results also show that the derived selection criteria can provide a better trade-off between the macro- and micro-averaged F measure, despite it is known that an increase in either of them is usually attained at the expense of the other one
Methods for Designing Multiple Classifier Systems
No full textIn the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains ope
Dynamic Classifier Selection
No full textAt present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of “dynamic classifier selection’ as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theoretical framework for dynamic classifier selection and to define the assumptions under which it can be expected to improve the accuracy of the individual classifiers. To this end, dynamic classifier selection is placed in the general framework of statistical decision theory and it is shown that, under some assumptions, the optimal Bayes classifier can be obtained by selecting non-optimal classifiers. Two classifier selection methods that derive from the proposed framework are described. The experimental results obtained in the classification of remote-sensing images and comparisons among different combination methods are reported
Supervised Classifier Combination trough Generalized Additive Multi-Model
No full textJ. Kittler, F. Roli (Eds.), Lecture Notes in Computer Scienc
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