TESI DOCTORAL - La Salle

Chapter 3. Hierarchical consensus architectures
Serial DHCA Parallel DHCA
Dataset % correct ΔRT % correct ΔRT
predictions (sec.) (%) predictions (sec.) (%)
Zoo 55.9 1.32 227.3 40.0 0.03 34.0
Iris 93.4 0.56 180.7 51.2 0.10 63.7
Wine 76.1 1.19 48.2 36.8 0.14 48.1
Glass 76.4 0.76 32.1 39.3 0.23 60.7
Ionosphere 83.2 11.9 33.1 47.9 1.38 46.5
WDBC 76.2 77.73 58.1 39.7 4.93 44.5
Balance 90.6 0.52 26.5 71.1 0.93 51.7
MFeat 88.6 5.40 27.4 68.4 5.83 28.9
average 80.0 12.57 78.0 49.3 1.70 47.3
Table 3.7: Evaluation of the minimum complexity DHCA variant estimation methodology
in terms of the percentage of correct predictions and running time penalizations resulting
from mistaken predictions.
architectures. The results corresponding to an averaging across 20 independent running
time estimation experiments are presented in table 3.7.
It can be observed that, in the serial case, SERTDHCA is a pretty accurate predictor,
achieving correct prediction rates of the computationally optimal consensus architecture
superior to 75% in all but one of the data sets. The running time overheads associated
to incorrect predictions are usually negligible in absolute terms (ΔRT (sec.)) —except for
the WDBC data collection, where the large real execution times of any of the consensus
architectures make any mistake costly.
As regards the performance of the proposed methodology for predicting the most efficent
parallel implementation of DHCA, its degree of accuracy is inferior than in the serial case –a
circumstance already observed in the context of RHCA–, although the penalization caused
by this lower level of precision ranges below one second of extra execution time, which
constitutes an assumable cost from a practical viewpoint —the WDBC and MFeat data
collections stand out as the exceptions to this rule, although the corresponding ΔRT (sec.)
overheads (around five seconds) are again of little importance in practice.
Finally, we have also evaluated the influence of employing the execution times of c>1
consensus executions for estimating the running times of the DHCA variants. Expectably,
the larger c, the more accurate the running time estimation and, consequently, smaller
running time overheads will be derived from incorrect predictions. On the flip side, however,
this will slow down the prediction process —recall that, in the experiments presented up to
now, c =1.
As in section 3.2.4, a sweep of values of c ∈ [1, 20] has been conducted, computing
the percentage of fastest consensus architecture correct predictions and the absolute and
relative running time deviations associated to prediction errors at each step of the sweep,
averaging the results of twenty independent runs of this experiment on each one of the eight
unimodal data collections —see figure 3.14.
It can be observed that, despite the gradual increase of correct predictions (figure
3.14(a)), the running time deviations suffer a steep decrease as soon as c = 4 consensus
processes are employed for computing SERTDHCA. Moreover, notice that using larger val-
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