Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Abstract

The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

Neuromarketing: Its current status and research perspectives

“…The Bibliometrix R-package was used to perform a bibliometric analysis, since it is an open source tool that facilitates scientific mapping (Aria & Cuccurullo, 2017). Several pieces of research in different disciplines that require scientific mapping have chosen to use it (Addor & Melsen, 2019;Almeida & de Paula, 2019;Bond & Buntins, 2018;Medina et al, 2018).…”

Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda

“…As defined by Callon et al (1991), centrality measures for a given cluster the intensity of its links with other clusters, in other words, it measures the degree of interaction of a network with other networks, whereas density measures the internal strength of the network, characterizing the strength of the links that tie the words making up the cluster together. Following Callon et al (1991), given both measures it is possible to identify four groups: in the first quadrant (upper right) we find the clusters known as motor clusters since they present strong centrality and high density (Cobo et al 2018); in the second (lower right corner) basic and transversal clusters; in the quadrant three (upper left corner) the highly developed but isolated and peripheral clusters, that are specialized but marginal; finally, the fourth quadrant (lower left) shows the emerging or declining clusters, characterized by low density and low centrality. Surprisingly, we find that in the first period 2008-2011 (Fig.…”

“…6). As already pointed out, this diagram allows to visualized a research field as a set of research themes (Cobo et al 2018), mapped along two axes (centrality on the abscissa and density on the ordinate); as centrality grows, the importance of external ties concerning the other themes grows, and the greater the density, the better the internal ties among the themes are developed.…”

Neuroeconomía: una revisión basada en técnicas de mapeo científico

“…La metodología se desarrolló en tres etapas. La primera consistió en realizar un análisis de mapeo científico, basado en los métodos bibliométricos sugeridos por Zupic y Čater (2015), usando la herramienta Bibliometrix (Aria & Cuccurullo, 2017), la cual ha sido empleada y validada en múltiples investigaciones (Addor & Melsen, 2019;Almeida & de Paula 2019;Bond & Buntins, 2018;Medina et al, 2018). Posteriormente, se realizó un análisis cronológico utilizando la metáfora de árbol, en la que se identificaron las subáreas del tema mediante las co-citaciones.…”