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In many situations, a centralized, conventional classification task can not be performed because the data is not available in a central facility. In such cases, we are dealing with distributed data mining problems, in which local models must be individually built and later combined into a consensus, global model. In this paper, we are particularly interested in distributed classification tasks with vertically partitioned data, i.e., when features are distributed among several sources. This restriction implies a challenging scenario given that the development of an accurate model usually requires access to all the features that are relevant for classification. To deal with such a situation, we propose an agent-based classification system, in which the preference orderings of each agent regarding the probability of an instance to belong to the target class are aggregated by means of social choice functions. We employ this method to classify microRNA target genes, an important bioinformatics problem, showing that the predictions derived from the social choice tend to outperform local models in this application. This performance gain is accompanied by other interesting advantages: the aggregation methods herein proposed are extremely simple, do not require transfer of large volumes of data, do not assume an offline training process or parameters setup, and preserve data privacy. [ABSTRACT FROM AUTHOR]

This article gives an introduction to agent‐based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom‐up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application‐oriented aspects, including selected tools and well‐known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well‐known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

With the increase in submission of sequences to public databases, the curators of these are not able to cope with the amount of information. The motivation of this work is to generate a system for automated annotation of data we are particularly interested in, namely proteins related to the Mycoplasmataceae family. Following previous works on automatic annotation using symbolic machine learning techniques, the present work proposes a method of automatic annotation of keywords (a part of the SWISS-PROT annotation procedure), and the validation, by an expert, of the annotation rules generated. The aim of this procedure is twofold: to complete the annotation of keywords of those proteins which is far from adequate, and to produce a prototype of the validation environment, which is aimed at an expert who does not have a deep knowledge of the structure of the current databases containing the necessary information s/he needs.

Network science has proved useful in analyzing structure and dynamics of social networks in several areas. This paper aims at analyzing the relationships of characters in Friends, a famous sitcom. In particular, two important aspects are investigated. First, the structure of the communities (groups) is examined to shed light on how different methods for community detection perform. Second, besides investigating the static structure of the graphs as well as causality relationships, also temporal aspects were examined. After all, this show was aired for 10 years and thus plots, roles, and friendship patterns among the characters seem to have changed. Furthermore, this sitcom is frequently associated with distinguishing prior assumptions such as: all six characters are equally prominent; it has no dominant storyline; friendship as surrogate family. This paper uses tools from network theory to check whether these and other such assumptions can be quantified and proved correct, especially considering the temporal aspect, i.e., what happens in the sitcom along time. The main findings regarding the centrality and temporal aspects are: patterns in graphs representing different time slices of the show change; overall, degrees of the six friends are indeed nearly the same; however, contrarily to what is believed, in different situations, the magnitudes of degree centrality do change; betweenness centrality differs significantly for each character thus some characters are better connectors than others; there is a high difference regarding degrees of the six friends versus the rest of the characters, which points to a centralized network; there are strong indications that the six friends are part of a surrogate family. As for the presence of groups (communities) within the network, methods of different natures were investigated and compared (pairwise and also using various metrics, including plausibility). The multilevel method performs reasonably in general. Also, it stands out that those methods do not always agree, resulting in groups that are very different from method to method.