TESI DOCTORAL - La Salle

CPU time (sec.)
CPU time (sec.)
16
14
12
10
8
6
4
16
14
12
10
8
6
4
2
0
CSPA
RHCA
DHCA
flat
CSPA
RHCA
DHCA
flat
CPU time (sec.)
CPU time (sec.)
1.5
1
0.5
0.6
0.5
0.4
0.3
0.2
0.1
EAC
RHCA
DHCA
flat
EAC
RHCA
DHCA
flat
CPU time (sec.)
CPU time (sec.)
18
16
14
12
10
8
6
4
HGPA
RHCA
DHCA
flat
CPU time (sec.)
12
11
10
Chapter 3. Hierarchical consensus architectures
9
8
7
6
MCLA
RHCA
DHCA
flat
CPU time (sec.)
16
14
12
10
8
6
4
2
0
ALSAD
RHCA
DHCA
flat
(a) Serial implementation running time
15
10
5
0
HGPA
RHCA
DHCA
flat
CPU time (sec.)
1.2
1
0.8
0.6
0.4
0.2
MCLA
RHCA
DHCA
flat
CPU time (sec.)
16
14
12
10
8
6
4
2
0
ALSAD
RHCA
DHCA
flat
(b) Parallel implementation running time
CPU time (sec.)
CPU time (sec.)
16
14
12
10
8
6
4
2
16
14
12
10
8
6
4
2
0
KMSAD
RHCA
DHCA
flat
KMSAD
RHCA
DHCA
flat
CPU time (sec.)
CPU time (sec.)
16
14
12
10
8
6
4
2
0
16
14
12
10
8
6
4
2
0
SLSAD
RHCA
DHCA
flat
SLSAD
RHCA
DHCA
flat
Figure 3.18: Running times of the computationally optimal RHCA, DHCA and flat consensus
architectures on the Zoo data collection for the diversity scenario corresponding to
a cluster ensemble of size l = 1596.
ensembles of size l = 1596, almost identical running time boxplot charts are obtained —see
figure 3.18.
As aforementioned, this same experiment has been conducted on the eleven remaining
unimodal data collections, and the corresponding execution time boxplot charts are
presented in appendix C.4. From their analysis, the following conclusions have been drawn:
– the prediction regarding the computationally optimality of flat or hierarhical consensus
architectures are made with a high degree of accuracy (in fact, average prediction
accuracies of 93.45% and 91.07% are obtained across our experiments in the serial
and parallel implementation cases, respectively).
– flat consensus is the most efficient consensus architecture when the EAC consensus
function is employed, regardless of the diversity scenario or whether the serial or
parallel implementation of hierarchical architectures is employed.
– in large datasets, only HGPA and MCLA are executable, as the time and space
complexities of the remaining consensus functions scale quadratically with the number
93