By Ireneusz Czarnowski (auth.), Ngoc Thanh Nguyen (eds.)
These Transactions post learn in computer-based tools of computational collective intelligence (CCI) and their purposes in quite a lot of fields comparable to the Semantic internet, social networks and multi-agent structures. TCCI strives to hide new methodological, theoretical and functional elements of CCI understood
as the shape of intelligence that emerges from the collaboration and pageant of lots of individuals (artificial and/or natural). the appliance of a number of computational intelligence applied sciences equivalent to fuzzy platforms, evolutionary computation, neural structures, consensus conception, etc., goals to aid human and different collective intelligence and to create new different types of CCI in common and/or synthetic systems.
This fourth factor includes a selection of 6 articles chosen from fine quality submissions. the 1st paper of Ireneusz Czarnowski entitled "Distributed studying with info aid" involves a hundred and twenty pages and has a monograph chracter. the second one half contains 5 ordinary papers adressing advances within the foundations and purposes of computational collective intelligence.
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Transactions on Computational Collective Intelligence IV
Those Transactions submit study in computer-based tools of computational collective intelligence (CCI) and their purposes in a variety of fields akin to the Semantic net, social networks and multi-agent structures. TCCI strives to hide new methodological, theoretical and functional elements of CCI understood because the kind of intelligence that emerges from the collaboration and festival of lots of individuals (artificial and/or natural).
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Example text
The above reasoning results also in approaches, where the instance situated close to the center of a cluster of similar instances should be selected as a prototype (see, for example, [6] and [215]). Such an approach requires using some clustering algorithms like, for example, k-means or fuzzy k-means algorithm [78], [144]. These algorithms generate cluster centers which are later considered to be the centroids and the reduced dataset is produced. In such an approach, a good reduced dataset can be obtained if the centroids are ”good” representatives of clusters in the data [135].
In the modified approach steps 1, 2 and 4 are identical as in the Algorithm 6. Step 3 is defined as mapping randomly input vectors from Dl into pl disjoint strata. Finally, the prototypes are selected from the obtained clusters (strata). The accuracy of many similarity-based methods is highly sensitive to the definition of the distance function. Among approaches to reduce this sensitivity are attribute selection or attribute weighting. The review of attribute selection and attribute weighting with respect to the accuracy of instance-based approaches can be found in [25].
N. (8) i=1 3. For examples from X, belonging to the class cl , (where l = 1, . . , k), calculate the value of its similarity coefficient Ii : n ∀x:xi,n+1 =cl Ii = xij sj , where i = 1, . . , N.