9,784 research outputs found
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 classifier’s 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
method’s credibility. The method’s 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: 2102–2021), “Smart Solutions in Ubiquitous
Computing Environments
Applying multiobjective evolutionary algorithms in industrial projects
During the recent years, multiobjective evolutionary algorithms have matured as a flexible optimization tool which can be used in various areas of reallife applications. Practical experiences showed that typically the algorithms need an essential adaptation to the specific problem for a successful application. Considering these requirements, we discuss various issues of the design and application of multiobjective evolutionary algorithms to real-life optimization problems. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. As a major issue, the handling of infeasible intermediate solutions is pointed out. Three application examples in the areas of constrained global optimization (electronic circuit design), semi-infinite programming (design centering problems), and discrete optimization (project scheduling) are discussed
Aeronautical engineering: A continuing bibliography, supplement 122
This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980
Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles
We present a group decision making framework for evaluating sustainability of the insulating materials. We tested thirteen materials on a model that was applied to retrofit a traditional rural building through roof's insulation. To evaluate the materials from the socio-economic and environmental viewpoints, we combined life cycle costing and assessment with an adaptive comfort evaluation. In this way, the performances of each coating material were measured in terms of an incurred reduction of costs and consumption of resources, maintenance of the cultural and historic significance of buildings, and a guaranteed indoor thermal comfort. The comprehensive assessment of the materials involved their assignment to one of the three preference-ordered sustainability classes. For this purpose, we used a multiple criteria decision analysis approach that accounted for preferences of a few tens of rural buildings' owners. The proposed methodological framework incorporated an outranking-based preference model to compare the insulating materials with the characteristic class profiles while using the weights derived from the revised Simos procedure. The initial sorting recommendation for each material was validated against the outcomes of robustness analysis that combined the preferences of individual stakeholders either at the output or at the input level. The analysis revealed that the most favorable materials in terms of their overall sustainability were glass wool, hemp fibres, kenaf fibres, polystyrene foam, polyurethane, and rock wool
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