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(a)
(b)
SVM with pairwise noise
SVM with uniform noise
(c)
(d)
Ripper with pairwise noise
Ripper with uniform noise
(e)
(f)
C4.5 with pairwise noise
C4.5 with uniform noise
Fig. 5.4 Accuracy over different amounts and types of noise. The different filters used are named
by their acronyms. “None” denotes the absence of any filtering. a SVMwithpairwisenoise b SVM
with uniform noise c Ripper with pairwise noise d Ripper with uniform noise e C4.5 with pairwise
noise f C4.5 with uniform noise
noise filter denoted by “None” is remarkably different from the lines that illustrate
the usage of any noise filter. The IPF filter is slightly better than the other due
to its greater sophistication, but in overall the use of filters is highly recommended.
Even in the case of 0% of controlled noise, the noise already present is also cleansed,
allowing the filtering to improve even in this base case. Please note that the divergence
appears even in the 5% case, showing that noise filtering is worth trying in low noise
frameworks.
Ripper obtains a lower overall accuracy than SVM, but the conclusions are akin:
the usage of noise filters is highly recommended as can be seen in Fig. 5.4 c, d. It is
remarkable that not applying filtering for Ripper causes a fast drop in performance,
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