8,922 research outputs found
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
Attribute reduction algorithm based on cognitive model of granular computing in inconsistent decision information systems
Cilj je ovoga rada istraĆŸiti novu metodu redukcije atributa u informacijskim sustavima nekonzistentne odluke. AnalizirajuÄi povezanost teorije redukcije atributa i kognitivne znanosti, u radu se predlaĆŸe algoritam redukcije atributa zasnovan na kognitivnom modelu granularnog raÄunanja. Analiza algoritma i numeriÄki eksperiment pokazuju vrijednost predloĆŸenog algoritma redukcije atributa. Ta se metoda moĆŸe primijeniti i na konzistentne i nekonzistentne sustave. PredloĆŸeni model takoÄer daje i novi model i naÄin razmiĆĄljanja za prouÄavanje povezanosti ljudske spoznaje i poimanja. Koristan je za razvoj kognitivnog modela.This article aims to explore a new method of attribute reduction in inconsistent decision information systems. By analyzing the connection of attribute reduction theory and cognitive science, an attribute reduction algorithm based on cognitive model of granular computing is proposed in this paper. Algorithm analysis and numerical experiment show the validity of the proposed attribute reductions algorithm. The method can be applied to both consistent and inconsistent information systems. The proposed model also provides a new model and thinking to study the connection of humanâs cognition and notion. It is useful to the development of cognitive model
An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour
in consensus reaching process under social network
group decision making is proposed, which is based on a theoretically
sound optimal feedback model. The manipulation
behaviour classification is twofold: (1) âindividual manipulationâ
where each expert manipulates his/her own behaviour to achieve
higher importance degree (weight); and (2) âgroup manipulationâ
where a group of experts force inconsistent experts to adopt
specific recommendation advices obtained via the use of fixed
feedback parameter. To counteract âindividual manipulationâ, a
behavioural weights assignment method modelling sequential
attitude ranging from âdictatorshipâ to âdemocracyâ is developed,
and then a reasonable policy for group minimum adjustment cost
is established to assign appropriate weights to experts. To prevent
âgroup manipulationâ, an optimal feedback model with objective
function the individual adjustments cost and constraints related
to the threshold of group consensus is investigated. This approach
allows the inconsistent experts to balance group consensus and
adjustment cost, which enhances their willingness to adopt the
recommendation advices and consequently the group reaching
consensus on the decision making problem at hand. A numerical
example is presented to illustrate and verify the proposed optimal
feedback model
Challenges and complexities in application of LCA approaches in the case of ICT for a sustainable future
In this work, three of many ICT-specific challenges of LCA are discussed.
First, the inconsistency versus uncertainty is reviewed with regard to the
meta-technological nature of ICT. As an example, the semiconductor technologies
are used to highlight the complexities especially with respect to energy and
water consumption. The need for specific representations and metric to
separately assess products and technologies is discussed. It is highlighted
that applying product-oriented approaches would result in abandoning or
disfavoring of new technologies that could otherwise help toward a better
world. Second, several believed-untouchable hot spots are highlighted to
emphasize on their importance and footprint. The list includes, but not limited
to, i) User Computer-Interfaces (UCIs), especially screens and displays, ii)
Network-Computer Interlaces (NCIs), such as electronic and optical ports, and
iii) electricity power interfaces. In addition, considering cross-regional
social and economic impacts, and also taking into account the marketing nature
of the need for many ICT's product and services in both forms of hardware and
software, the complexity of End of Life (EoL) stage of ICT products,
technologies, and services is explored. Finally, the impact of smart management
and intelligence, and in general software, in ICT solutions and products is
highlighted. In particular, it is observed that, even using the same
technology, the significance of software could be highly variable depending on
the level of intelligence and awareness deployed. With examples from an
interconnected network of data centers managed using Dynamic Voltage and
Frequency Scaling (DVFS) technology and smart cooling systems, it is shown that
the unadjusted assessments could be highly uncertain, and even inconsistent, in
calculating the management component's significance on the ICT impacts.Comment: 10 pages. Preprint/Accepted of a paper submitted to the ICT4S
Conferenc
Granular computing based approach of rule learning for binary classification
Rule learning is one of the most popular types of machine-learning approaches, which typically follow two main strategies: âdivide and conquerâ and âseparate and conquerâ. The former strategy is aimed at induction of rules in the form of a decision tree, whereas the latter one is aimed at direct induction of ifâthen rules. Due to the case that the divide and conquer strategy could result in the replicated sub-tree problem, which not only leads to overfitting but also increases the computational complexity in classifying unseen instances, researchers have thus been motivated to develop rule learning approaches through the separate and conquer strategy. In this paper, we focus on investigation of the Prism algorithm, since it is a representative one that follows the separate and conquer strategy, and is aimed at learning a set of rules for each class in the setting of granular computing, where each class (referred to as target class) is viewed as a granule. The Prism algorithm shows highly comparable performance to the most popular algorithms, such as ID3 and C4.5, which follow the divide and conquer strategy. However, due to the need to learn a rule set for each class, Prism usually produces very complex rule-based classifiers. In real applications, there are many problems that involve one target class only, so it is not necessary to learn a rule set for each class, i.e., only a set of rules for the target class needs to be learned and a default rule is used to indicate the case of non-target classes. To address the above issues of Prism, we propose a new version of the algorithm referred to as PrismSTC, where âSTCâ stands for âsingle target classâ. Our experimental results show that PrismSTC leads to production of simpler rule-based classifiers without loss of accuracy in comparison with Prism. PrismSTC also demonstrates sufficiently good performance comparing with C4.5
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