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classifiers are different we talk of Multiple Classifier Systems (MCSs). They are thus
a generalization of the classic ensembles and they should offer better improvements
in noisy environments. They are tackled in Sect.
5.4.1
.
We can separate the labeled instances in several “bags” or groups, each one con-
taining only those instances belonging to the same class. This type of decomposition
is well suited for those classifiers that can only work with binary classification prob-
lems, but has also been suggested that this decomposition can help to diminish the
effects of noise. This decomposition is expected to decrease the overlapping between
the classes and to limit the effect of noisy instances to their respective bags by sim-
plifying the problem and thus alleviating the effect of the noise if the whole data set
were considered.
5.4.1 Multiple Classifier Systems for Classification Tasks
Given a set of problems, finding the best overall classification algorithm is sometimes
difficult because some classifiers may excel in some cases and perform poorly in
others. Moreover, even though the optimal match between a learning method and
a problem is usually sought, this match is generally difficult to achieve and perfect
solutions are rarely found for complex problems [
34
,
36
]. This is one reason for using
Multi-Classifier Systems [
34
,
36
,
72
], since it is not necessary to choose a specific
learning method. All of themmight be used, taking advantage of the strengths of each
method, while avoiding its weaknesses. Furthermore, there are other motivations to
combine several classifiers [
34
]:
•
To avoid the choice of some arbitrary but important initial conditions, e.g. those
involving the parameters of the learning method.
•
To introduce some randomness to the training process in order to obtain different
alternatives that can be combined to improve the results obtained by the individual
classifiers.
•
To use complementary classification methods to improve dynamic adaptation and
flexibility.
Several works have claimed that simultaneously using classifiers of different
types, complementing each other, improves classification performance on difficult
problems, such as satellite image classification [
60
], fingerprint recognition [
68
] and
foreign exchange market prediction [
73
]. Multiple Classifier Systems [
34
,
36
,
72
,
94
] are presented as a powerful solution to these difficult classification problems,
because they build several classifiers from the same training data and therefore allow
the simultaneous usage of several feature descriptors and inference procedures. An
important issue when using MCSs is the way of creating
diversity
among the clas-
sifiers [
54
], which is necessary to create discrepancies among their decisions and
hence, to take advantage of their combination.
MCSs have been traditionally associated with the capability of working accu-
rately with problems involving noisy data [
36
]. The main reason supporting this
