83,675 research outputs found
Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?
To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not
to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy
min-max neural network with its basic learning procedure is used within six different algorithm independent learning
schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation
of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on
unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four
criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability
to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning
scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions
examined is whether and when to use a single classifier or a combination of a number of component classifiers within a
multiple classifier system
Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability
In this paper a combination of neuro-fuzzy
classifiers for improved classification performance and reliability
is considered. A general fuzzy min-max (GFMM) classifier with
agglomerative learning algorithm is used as a main building
block. An alternative approach to combining individual classifier
decisions involving the combination at the classifier model level is
proposed. The resulting classifier complexity and transparency is
comparable with classifiers generated during a single crossvalidation
procedure while the improved classification
performance and reduced variance is comparable to the ensemble
of classifiers with combined (averaged/voted) decisions. We also
illustrate how combining at the model level can be used for
speeding up the training of GFMM classifiers for large data sets
Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems
Combining classifiers by majority voting (MV) has
recently emerged as an effective way of improving
performance of individual classifiers. However, the
usefulness of applying MV is not always observed and
is subject to distribution of classification outputs in a
multiple classifier system (MCS). Evaluation of MV
errors (MVE) for all combinations of classifiers in MCS
is a complex process of exponential complexity.
Reduction of this complexity can be achieved provided
the explicit relationship between MVE and any other
less complex function operating on classifier outputs is
found. Diversity measures operating on binary
classification outputs (correct/incorrect) are studied in
this paper as potential candidates for such functions.
Their correlation with MVE, interpreted as the quality
of a measure, is thoroughly investigated using artificial
and real-world datasets. Moreover, we propose new
diversity measure efficiently exploiting information
coming from the whole MCS, rather than its part, for
which it is applied
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