  The outcome of a collective decision-making process, such as crowdsourcing,
often relies on the procedure through which the perspectives of its individual
members are aggregated. Popular aggregation methods, such as the majority rule,
often fail to produce the optimal result, especially in high-complexity tasks.
Methods that rely on meta-cognitive information, such as confidence-based
methods and the Surprisingly Popular Option, had shown an improvement in
various tasks. However, there is still a significant number of cases with no
optimal solution. Our aim is to exploit meta-cognitive information and to learn
from it, for the purpose of enhancing the ability of the group to produce a
correct answer. Specifically, we propose two different feature-representation
approaches: (1) Response-Centered feature Representation (RCR), which focuses
on the characteristics of the individual response instances, and (2)
Answer-Centered feature Representation (ACR), which focuses on the
characteristics of each of the potential answers. Using these two
feature-representation approaches, we train Machine-Learning (ML) models, for
the purpose of predicting the correctness of a response and of an answer. The
trained models are used as the basis of an ML-based aggregation methodology
that, contrary to other ML-based techniques, has the advantage of being a
"one-shot" technique, independent from the crowd-specific composition and
personal record, and adaptive to various types of situations. To evaluate our
methodology, we collected 2490 responses for different tasks, which we used for
feature engineering and for the training of ML models. We tested our
feature-representation approaches through the performance of our proposed
ML-based aggregation methods. The results show an increase of 20% to 35% in the
success rate, compared to the use of standard rule-based aggregation methods.
