  Multiple query criteria active learning (MQCAL) methods have a higher
potential performance than conventional active learning methods in which only
one criterion is deployed for sample selection. A central issue related to
MQCAL methods concerns the development of an integration criteria strategy
(ICS) that makes full use of all criteria. The conventional ICS adopted in
relevant research all facilitate the desired effects, but several limitations
still must be addressed. For instance, some of the strategies are not
sufficiently scalable during the design process, and the number and type of
criteria involved are dictated. Thus, it is challenging for the user to
integrate other criteria into the original process unless modifications are
made to the algorithm. Other strategies are too dependent on empirical
parameters, which can only be acquired by experience or cross-validation and
thus lack generality; additionally, these strategies are counter to the
intention of active learning, as samples need to be labeled in the validation
set before the active learning process can begin. To address these limitations,
we propose a novel MQCAL method for classification tasks that employs a third
strategy via weighted rank aggregation. The proposed method serves as a
heuristic means to select high-value samples of high scalability and generality
and is implemented through a three-step process: (1) the transformation of the
sample selection to sample ranking and scoring, (2) the computation of the
self-adaptive weights of each criterion, and (3) the weighted aggregation of
each sample rank list. Ultimately, the sample at the top of the aggregated
ranking list is the most comprehensively valuable and must be labeled. Several
experiments generating 257 wins, 194 ties and 49 losses against other
state-of-the-art MQCALs are conducted to verify that the proposed method can
achieve superior results.
