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  • Hindawi Limited  (2)
  • Mathematics  (2)
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  • Hindawi Limited  (2)
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  • Mathematics  (2)
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  • 1
    Online Resource
    Online Resource
    Hindawi Limited ; 2021
    In:  Scientific Programming Vol. 2021 ( 2021-11-11), p. 1-16
    In: Scientific Programming, Hindawi Limited, Vol. 2021 ( 2021-11-11), p. 1-16
    Abstract: Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2070004-0
    Location Call Number Limitation Availability
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  • 2
    Online Resource
    Online Resource
    Hindawi Limited ; 2008
    In:  Scientific Programming Vol. 16, No. 1 ( 2008), p. 31-47
    In: Scientific Programming, Hindawi Limited, Vol. 16, No. 1 ( 2008), p. 31-47
    Abstract: Discovering gene co-regulatory relationships is one of most important research in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest (called target gene), identify the condition subsets where strong gene co-regulations of the target gene are observed and its co-regulated genes in these condition subsets. The co-regulations are local in the sense that they occur in some subsets of full experimental conditions. The study on this problem can contribute to better understanding and characterizing the target gene during the biological activity involved. In this paper, we propose an innovative method for finding gene specific co-regulations using genetic algorithm (GA). A sliding window is used to delimit the allowed length of conditions in which gene co-regulations occur and an ad hoc GA, called the progressive GA, is performed in each window position to find those condition subsets having high fitness. It is called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in its preceding window position, enabling the GA to achieve a better accuracy than the non-progressive algorithm. k NN Lookup Table is utilized to substantially speed up fitness evaluation in the GA. Experimental results with a real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific co-regulations.
    Type of Medium: Online Resource
    ISSN: 1058-9244 , 1875-919X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2008
    detail.hit.zdb_id: 2070004-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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