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Multiple query criteria active learning (MQCAL) methods have a higherpotential performance than conventional active learning methods in which onlyone criterion is deployed for sample selection. A central issue related toMQCAL methods concerns the development of an integration criteria strategy(ICS) that makes full use of all criteria. The conventional ICS adopted inrelevant research all facilitate the desired effects, but several limitationsstill must be addressed. For instance, some of the strategies are notsufficiently scalable during the design process, and the number and type ofcriteria involved are dictated. Thus, it is challenging for the user tointegrate other criteria into the original process unless modifications aremade to the algorithm. Other strategies are too dependent on empiricalparameters, which can only be acquired by experience or cross-validation andthus lack generality; additionally, these strategies are counter to theintention of active learning, as samples need to be labeled in the validationset before the active learning process can begin. To address these limitations,we propose a novel MQCAL method for classification tasks that employs a thirdstrategy via weighted rank aggregation. The proposed method serves as aheuristic means to select high-value samples of high scalability and generalityand is implemented through a three-step process: (1) the transformation of thesample selection to sample ranking and scoring, (2) the computation of theself-adaptive weights of each criterion, and (3) the weighted aggregation ofeach sample rank list. Ultimately, the sample at the top of the aggregatedranking list is the most comprehensively valuable and must be labeled. Severalexperiments generating 257 wins, 194 ties and 49 losses against otherstate-of-the-art MQCALs are conducted to verify that the proposed method canachieve superior results.

A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria