IEEE Computational Intelligence Magazine - May 2022 - 72

of the average computational cost
with the sampling rate on the a9a
dataset, where the computational cost
is obtained by normalizing the computational
time of different sampling
rates w.r.t. sampling rate 0.1. It can be
seen from these results that the computational
cost of each simulation
increases with the number of instances
in a flat manner, which is consistent
with the theoretical computation
time. Therefore, AUC optimization
can be regarded as an expensive optimization
problem when the dataset
is large.
V. EMTAUC
This section describes the detail of
our proposed EMTAUC. Specifically,
the cheap task's construction method
is first given, providing a strong guarantee
for the multitasking environment.
Next, the knowledge available
between the constructed cheap task
and the expensive task is discussed.
Then, dynamic adjustment strategies
of the cheap task are presented.
Finally, the framework of EMTAUC
is introduced.
A. Construction of a Multitasking
AUC Optimization Environment
EMTO is one of the emerging areas
of the computational
intelligence
community, which shows that optimizing
multiple tasks simultaneously
with effective knowledge transfer
helps improve the convergence characteristics
of each task. Inspired by
this idea, this section designs a multitasking
AUC optimization environment
that includes cheap tasks and
expensive tasks, as shown in Fig. 2, to
utilize the information interaction
between tasks to accelerate the convergence
of expensive optimization
tasks. In a multitasking AUC optimization
environment, optimizing AUC
on the whole dataset is regarded as
an expensive task (AUCE) and optimizing
AUC on the sampled dataset
is considered as a cheap task (AUCC).
Therefore, the objective function of
multi-task AUC optimization can be
defined as follows:
72 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022
Features
Cheap Task:
Optimize AUC on the
Sampled Dataset
x1
+
Dataset
Items
⋮
xT
++
x1
-
⋮
Features
xT
- +
⋮
Expensive Task:
Optimize AUC on
the Whole Dataset
Items
⋮
xT
- +
FIGURE 2 Construction of a multitasking AUC optimization environment.
xT
++
x1
-
x1
+
Design
Knowledge
Transfer
Dynamic
Adjustment
Items
Items
Features
⋮
⋮
x1
-
xs×T
-
+
Features
⋮
⋮
x1
+
xs×T
+
+
x1
+
xs×T
+
x1
-
xs×T
-
+
+

IEEE Computational Intelligence Magazine - May 2022

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