Unsupervised classification for uncertain varying responses: The wisdom-in-the-crowd (WICRO) algorithm

Distinguishing the Leading Agents in Classification Problems Using the Entropy-Based Metric

“…To avoid such influence, in more sophisticated methods [10][11][12][13][14], the problem is divided into two stages. First, using the agents' classifications γ k , k = 1, 2, .…”

“…As indicated above, there exist several algorithms of classification that implement the difference between the opinions of competent and non-competent agents [10][11][12][13]. In particular, the Distance-Based Collaborative Classification (DBCC) algorithm [12] directly considers the normalized Hamming distance d H (γ k , γ k ′ |j) between the partitions…”

“…Following this assumption, let us consider the other example and form a group of leaders starting with the agents' expertise [13]. Assume that the group A of l = 8 agents classifies n = 9 items x from the set X over m = 5 classes C. The agents' classifications are shown in Table 3.…”

“…Assume that the group A of l = 8 agents classifies n = 9 items x from the set X over m = 5 classes C. The agents' classifications are shown in Table 3. By the Wisdom in the Crowd (WICRO) algorithm [13], the agents are divided into clusters based on the number of agreements about the classes for each item x. The clusters obtained by the agents are summarized in Table 4.…”

“…Another approach to selecting the competent agents was implemented in the algorithms [12,13], which are based on the similarities of the agents' classifications, and in the algorithm [14], where the competent agents are selected using the expectation bias [15].…”