34,573 research outputs found
Incorporating stakeholdersâ knowledge in group decision-making
International audienc
Rough Set Applied to Air Pollution: A New Approach to Manage Pollutions in High Risk Rate Industrial Areas
This study presents a rough set application, using together the ideas of classical rough set approach, based on the indiscernibility relation and the dominance-based rough set approach (DRSA), to air micro-pollution management in an industrial site with a high environmental risk rate, such as the industrial area of Syracuse, located in the South of Italy (Sicily). This new data analysis tool has been applied to different decision problems in various fields with considerable success, since it is able to deal both with quantitative and with qualitative data and the results are expressed in terms of decision rules understandable by the decision-maker. In this chapter, some issue related to multi-attribute sorting (i.e. preference-ordered classification) of air pollution risk is presented, considering some meteorological variables, both qualitative and quantitative as attributes, and criteria describing the different objects (pollution occurrences) to be classified, that is, different levels of sulfur oxides (SOx), nitrogen oxides (NOx), and methane (CH4) as pollution indicators. The most significant results obtained from this particular application are presented and discussed: examples of âif, ⊠thenâ decision rules, attribute relevance as output of the data analysis also in terms of exchangeable or indispensable attributes/criteria, of qualitative substitution effect and interaction between them
Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection
This study proposes an alternate data extraction method that combines three well-known
feature selection methods for handling large and problematic datasets: the correlation-based feature
selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods.
This study aims to enhance the classifierâs performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several
phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks.
Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve
the computational time complexity and increase the classification accuracy. Several datasets with
various characteristics and volumes were used in the experimental process to evaluate the proposed
methodâs credibility. The methodâs performance was validated using standard evaluation measures
and benchmarked with other established methods such as deep learning (DL). Overall, the proposed
work proved that it could assist the classifier in returning a significant result, with an accuracy rate
of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM),
which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical
result indicates that the proposed method is an alternative extraction tool for those with difficulties
acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty
of Informatics and Management, Czech Republic (ID: 2102â2021), âSmart Solutions in Ubiquitous
Computing Environments
Towards a Reliable Framework of Uncertainty-Based Group Decision Support System
This study proposes a framework of Uncertainty-based Group Decision Support
System (UGDSS). It provides a platform for multiple criteria decision analysis
in six aspects including (1) decision environment, (2) decision problem, (3)
decision group, (4) decision conflict, (5) decision schemes and (6) group
negotiation. Based on multiple artificial intelligent technologies, this
framework provides reliable support for the comprehensive manipulation of
applications and advanced decision approaches through the design of an
integrated multi-agents architecture.Comment: Accepted paper in IEEE-ICDM2010; Print ISBN: 978-1-4244-9244-
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