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- aggregation classification "P1".
- aggregation creator B74744.
- aggregation creator B74745.
- aggregation creator B74746.
- aggregation creator B74747.
- aggregation creator person.
- aggregation date "2009".
- aggregation format "application/pdf".
- aggregation hasFormat 2918812.bibtex.
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- aggregation isPartOf urn:isbn:9783642029615.
- aggregation isPartOf urn:issn:0302-9743.
- aggregation language "eng".
- aggregation publisher "Springer".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Science General".
- aggregation title "Integrating rough sets with neural networks for weighting road safety performance indicators".
- aggregation abstract "This paper aims at improving two main uncertain factors in neural networks training in developing a composite road safety performance indicator. These factors are the initial value of network weights and the iteration time. More specially, rough sets theory is applied for rule induction and feature selection in decision situations, and the concepts of reduct and core are utilized to generate decision rules from the data to guide the self-training of neural networks. By means of simulation, optimal weights are assigned to seven indicators in a road safety data set for 21 European countries. Countries are ranked in terms of their composite indicator score. A comparison study shows the feasibility of this hybrid framework for road safety performance indicators.".
- aggregation authorList BK190681.
- aggregation endPage "67".
- aggregation startPage "60".
- aggregation volume "5589".
- aggregation aggregates 2955902.
- aggregation isDescribedBy 2918812.
- aggregation similarTo 978-3-642-02962-2_8.
- aggregation similarTo LU-2918812.