Filtreler

2012 - 20183
Filtreleri Sıfırla

Ayarlar

Yazar: Yıldız, Olcay Taner × Scopus Q: Q2 ×

Arama Sonuçları

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Yayın
    Design and analysis of classifier learning experiments in bioinformatics: survey and case studies
    (IEEE Computer Soc, 2012-12) İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem
    In many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design of such experiments and the analysis of the resulting data using statistical tests should be done carefully for the results to carry significance. In this paper, we first review the performance measures used in classification, the basics of experiment design and statistical tests. We then give the results of our survey over 1,500 papers published in the last two years in three bioinformatics journals (including this one). Although the basics of experiment design are well understood, such as resampling instead of using a single training set and the use of different performance metrics instead of error, only 21 percent of the papers use any statistical test for comparison. In the third part, we analyze four different scenarios which we encounter frequently in the bioinformatics literature, discussing the proper statistical methodology as well as showing an example case study for each. With the supplementary software, we hope that the guidelines we discuss will play an important role in future studies.
  • Küçük Resim
    Yayın
    Constructing a WordNet for Turkish using manual and automatic annotation
    (Assoc Computing Machinery, 2018-05) Ehsani, Razieh; Solak, Ercan; Yıldız, Olcay Taner
    In this article, we summarize the methodology and the results of our 2-year-long efforts to construct a comprehensive WordNet for Turkish. In our approach, we mine a dictionary for synonym candidate pairs and manually mark the senses in which the candidates are synonymous. We marked every pair twice by different human annotators. We derive the synsets by finding the connected components of the graph whose edges are synonym senses. We also mined Turkish Wikipedia for hypernym relations among the senses. We analyzed the resulting WordNet to highlight the difficulties brought about by the dictionary construction methods of lexicographers. After splitting the unusually large synsets, we used random walk-based clustering that resulted in a Zipfian distribution of synset sizes. We compared our results to BalkaNet and automatic thesaurus construction methods using variation of information metric. Our Turkish WordNet is available online.
  • Küçük Resim
    Yayın
    Univariate decision tree induction using maximum margin classification
    (Oxford Univ Press, 2012-03) Yıldız, Olcay Taner
    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. For two-class data sets, it generates significantly smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass data sets with one-vs-rest methodology.