Page 100 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

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An advantage of median is that it is not sensitive to outliers. This can be especially
relevant in cases where respondents answer without any relevant knowledge or if they
do not give much thought to their judgment [8].

4.4 Weighted aggregation

Weighted aggregation is a technique used in prediction that involves assigning a
weight or importance factor to each individual's prediction. The weight is determined
based on several factors such as their credibility, expertise, or past performance. In
the study described in [21], the authors assigned weights based on self-assessment,
knowledge, and hit rate in their research. On the other hand, Nguyen VD et al. in [22]
assigned weights based on the individual's reputation. Some studies used different
methods such as the accuracy of recent judgments [23], or summing probability
predictions [24].
Weighting the crowd produced both positive and negative outcomes. When relying
on the confidence of individual members, the crowd did not exhibit wisdom, as their
personal biases led to overconfidence [12]. However, von der Gracht et al. [21] found
that the individuals were not overconfident and exhibited more wisdom compared to
individuals aggregated by their past performance. Despite the favorable outcomes,
they did not discover any added benefits in weighting the crowd since equally
weighting the responses resulted in superior outcomes. In their study presented in
[25], the authors demonstrated that by removing underperforming individuals from
the group and only considering those who made a positive contribution (model
CWM), the overall performance of the crowd (which is highlighted in bold in Fig 3)
improved significantly. This approach was found to be more effective than relying
solely on past performance for weighting.

Fig. 3. Performance of the Models Compared (in Terms of Their Scores) [25]

Overall, the method of assigning weights to individuals depends on various factors
such as the type of data being analyzed and the objectives of the research.

4.5 Mode

The mode can be a useful measure of central tendency when dealing with categorical
data, such as words or other non-numerical values. For example, it can be used to
determine which football player or team performs best [26], or which political party
will win the election [27]. In cases where the data is categorical, it may not be

CITA 2023 ISBN: 978-604-80-8083-9