Dealing with Noisy Data - Data Preprocessing in Data Mining - page 133

focusing in gaussian noise the robustness results are better than those of the uniform
noise. The main difference in this case is that MCS3-1 and MCS5 are not statistically
worse than C4.5.
5.5.4.3 Conclusions
The results obtained have shown that the MCSs studied do not always significantly
improve the performance of their single classification algorithms when dealing with
noisy data, although they do in the majority of cases (if the individual components
are not heavily affected by noise). The improvement depends on many factors, such
as the type and level of noise. Moreover, the performance of the MCSs built with
heterogeneous classifiers depends on the performance of their single classifiers, so
it is recommended that one studies the behavior of each single classifier before
building the MCS. Generally, the MCSs studied are more suitable for class noise
than for attribute noise. Particularly, they perform better with the most disruptive
class noise scheme (the uniform one) and with the least disruptive attribute noise
scheme (the gaussian one).
The robustness results show that the studied MCS built with heterogeneous clas-
sifiers will not be more robust than the most robust among their single classification
algorithms. In fact, the robustness can always be shown as an average of the robust-
ness of the individual methods. The higher the robustness of the individual classifiers
are, the higher the robustness of the MCS is.
5.5.5 Analysis of the OVO Decomposition with Noise
In this section, the performance and robustness of the classification algorithms using
the OVO decomposition with respect to its baseline results when dealing with data
suffering from noise are analyzed. In order to investigate whether the decomposi-
tion is able to reduce the effect of noise or not, a large number of data sets are
created introducing different levels and types of noise, as suggested in the litera-
ture. Several well-known classification algorithms, with or without decomposition,
are trained with them in order to check when decomposition is advantageous. The
results obtained show that methods using the One-vs-One strategy lead to better per-
formances and more robust classifiers when dealing with noisy data, especially with
the most disruptive noise schemes. Section 5.5.5.1 is devoted to the study of the class
noise scheme, whereas Sect. 5.5.5.2 analyzes the attribute noise case.
5.5.5.1 First Scenario: Data Sets with Class Noise
Table 5.8 shows the test accuracy and RLA results for each classification algorithm
at each noise level along with the associated p-values between the OVO and the