Browsing by Subject "Ensemble of classifiers"

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    Conceptfusion: A flexible scene classification framework
    (Springer, 2015-03-04) Saraç, Mustafa İlker; işcen, Ahmet; Gölge, Eren; Duygulu, Pınar
    We introduce ConceptFusion, a method that aims high accuracy in categorizing large number of scenes, while keeping the model relatively simpler and efficient for scalability. The proposed method combines the advantages of both low-level representations and high-level semantic categories, and eliminates the distinctions between different levels through the definition of concepts. The proposed framework encodes the perspectives brought through different concepts by considering them in concept groups that are ensembled for the final decision. Experiments carried out on benchmark datasets show the effectiveness of incorporating concepts in different levels with different perspectives. © Springer International Publishing Switzerland 2015.
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    Squeezing the ensemble pruning: Faster and more accurate categorization for news portals
    (Springer, 2012) Toraman, Cağrı; Can, Fazlı
    Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC). In this study, we analyze how ensemble pruning can improve text categorization efficiency in time-critical real-life applications such as news portals. The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications. We conduct experiments on ensemble pruning-based news article categorization to measure its accuracy and time cost. The results show that our heuristics reduce the time cost of the second phase. Also we can make a trade-off between accuracy and time cost to improve both of them with appropriate pruning degrees. © 2012 Springer-Verlag Berlin Heidelberg.