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  • 1. Dorfman, M.V
    et al.
    Ganul, V.L.
    Girko, V.L
    Pavlenko, Tatjana
    Questionnaire-based determination of groups at high risk for lung cancer (in Russian)1990In: Problems of Oncology (rus), ISSN 0507-3758, Vol. 36, no 12, p. 1469-1473Article in journal (Refereed)
    Abstract [en]

    The factor analysis of qualitative parameters carried out in 968 patients with lung cancer helped identify certain features which may play the key role in the development and, consequently, diagnosis of various types of the disease. In workers of major industries, smoking proved a significant factor of higher incidence of lung cancer. Formation of the habit at an earlier age, its intensity and concomitant occupational hazards were found to increase the risk of cancer, particularly, in males. Mass screenings and questionnaire--based interviewing of smokers are suggested. Young age and heavy smokers should be included in groups at risk irrespective of

  • 2. Girko, V. L.
    et al.
    Pavlenko, Tatjana
    An asymptotically normal G-estimate for the Anderson-Fisher discriminant function.: (translation from Vychisl. Prikl. Mat. 70, 128-132 (1990))1994In: Journal of Mathematical Sciences, ISSN 1072-3374, Vol. 70, no 1, p. 1593-1595Article in journal (Refereed)
  • 3. Girko, V. L.
    et al.
    Pavlenko, Tatjana
    Asymptotic normality of a new estimator of the Mahalanobis distance.: (translation from Teor. Veroyatn. Mat. Stat., Kiev 41, 29-33 (1989))1990In: Theory of Probability and Mathematical Statistics, ISSN 0094-9000, Vol. 41, p. 33-36Article in journal (Refereed)
    Abstract [en]

    This paper deals with the construction and properties of a Mahalanobis distance estimator under fulfillment of the Kolmogorov conditions. Consistency and asymptotic normality of the estimator are proved.

  • 4. Girko, V. L
    et al.
    Pavlenko, Tatjana
    G-estimates of the quadratic discriminant function. (Russian, English)1989In: Ukrains'kyi Matematychnyi Zhurnal: naukovyi zhurnal, ISSN 0041-6053, Vol. 41, no 12, p. 1700-1705Article in journal (Refereed)
    Abstract [en]

    Properties of a G (``general'') estimate of square loss for discriminant analysis in problems with two normal distributions are studied. If Kolmogorov's asymptotic property holds then the G-estimate is asymptotically normal.

  • 5. Girko, V. L.
    et al.
    Pavlenko, Tatjana
    The G-estimate of the regularized Mahalanobis distance in the case where the distribution of observations is different from the normal one. (Russian. English summary)1989In: Akademiya Nauk Ukrainskoi S.S.R. Doklady. Seriya A. Fiziko-Matematicheskie i Tekhnicheskie Nauki (Ukraine), ISSN 0201-8446, Vol. 11, p. 61-64Article in journal (Refereed)
    Abstract [en]

    G-estimation of the regular Mahalanobis distance under the condition that the dimension of the sample space is comparable with the size of the learning samples is analysed. It is proved for the first time that properties of consistency and asymptotic normality of this estimation hold, if the distribution of observations is not normal.

  • 6. Pavlenko, Tatjana
    Asymptotic behavior of the probabilities of misclassification for discriminant function with weighting1997Report (Other academic)
  • 7.
    Pavlenko, Tatjana
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Asymptotic error rates in the discriminant analysis using feature selection2001In: 23 European Meeting of Statisticians: Contributed Papers II, Lisboa: Instituto Nacional de Estatística , 2001, p. 307-308Conference paper (Refereed)
  • 8. Pavlenko, Tatjana
    Augmented naive BN classifier in a high-dimensional framework2002In: European Meeting of Statisticians, Prague 2002, 2002Conference paper (Refereed)
  • 9. Pavlenko, Tatjana
    Discriminant analysis with growing dimension1997Licentiate thesis, monograph (Other scientific)
  • 10. Pavlenko, Tatjana
    Feature informativeness, curse-of-dimensionality and error probability in discriminant analysis.2001Doctoral thesis, monograph (Other scientific)
  • 11.
    Pavlenko, Tatjana
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Feature informativeness in high-dimensional discriminant analysis2003In: Communications in Statistics: Theory and Methods, ISSN 0361-0926, Vol. 32, no 2, p. 459-474Article in journal (Refereed)
    Abstract [en]

    A concept of feature informativeness was introduced as a way of measuring the discriminating power of a set of features. A question of interest is how this property of features affects the discrimination performance. The effect is assessed by means of a weighted discriminant function, which distributes weights among features according to their informativeness. The asymptotic normality of the weighted discriminant function is proven and the limiting expressions for the errors are obtained in the growing dimension asymptotic framework, i.e., when the number of features is proportional to the sample size. This makes it possible to establish the optimal in a sense of minimum error probability type of weighting.

  • 12. Pavlenko, Tatjana
    G-estimation of the Mahalanobis distance for the case of an arbitrary continuous distribution of observed vectors. (Russian. English summary)1988In: [J] Tr. Vychisl. Tsentra, Tartu, Vol. 56, p. 50-58Article in journal (Refereed)
  • 13. Pavlenko, Tatjana
    On assessing teh feature informativeness in high-dimensional discriminant analysis: Third Ukrainian-Scandinavian conference in Probability Theory and Mathematical Statistics: Contributed Papers1999Conference paper (Other academic)
  • 14.
    Pavlenko, Tatjana
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    On feature selection, curse-of-dimensionality and error probability in discriminant analysis2003In: Journal of statistical planning and inference, ISSN 0378-3758, Vol. 115, no 2, p. 565-584Article in journal (Refereed)
    Abstract [en]

    Discrimination performance, measured by the limiting error probability, is considered from the point of view of feature discriminating power. For assessing the latter, a concept of feature informativeness is introduced. A threshold feature selection technique is considered. Selection is incorporated into the discriminant function by means of an inclusion-exclusion factor which eliminates the sets of features whose informativeness do not exceed a given threshold. An issue is how this selection procedure affects the error rate when sample based estimates are used in the discriminant function. This effect is evaluated in a growing dimension asymptotic framework. In particular, the increase of the moments of the discriminant function induced by the curse-of-dimensionality is shown together with the effect of the threshold-based feature selection. The asymptotic normality of the discriminant function, which makes it possible to express the overall error probability in a closed form and view it as a function of a given threshold of selection.

  • 15. Pavlenko, Tatjana
    Variable informativeness in discriminant analysis1998Report (Other academic)
  • 16.
    Pavlenko, Tatjana
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Dietrich, von Rosen
    Bayesian Network Classifiers in a High Dimensional Framework2002In: UAI'02: Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, University of Alberta, Edmonton, Alberta, Canada, August 1-4, 2002., Morgan Kaufmsnn , 2002, p. 397-404Conference paper (Refereed)
    Abstract [en]

    We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classifiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these classifiers have an important advantage: The corresponding discriminant function turns out to be a specialized case of a generalized additive model, which makes it possible to get closed form expressions for the asymptotic misclassification probabilities used here as a measure of classification accuracy. Moreover, in this paper we propose a new quantity for assessing the discriminative power of a set of features which is then used to elaborate the augmented naive Bayes classifier. The result is a weighted form of the augmented naive Bayes that distributes weights among the sets of features according to their discriminative power. We derive the asymptotic distribution of the sample based discriminative power and show that it is seriously overestimated in a high dimensional case. We then apply this result to find the optimal, in a sense of minimum misclassification probability, type of weighting.

  • 17.
    Pavlenko, Tatjana
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Fridén, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Scoring Feature Subsets for Separation power in Supervised Bayes Classification2006In: Advances in Intelligent and Soft Computing, ISSN 1867-5662, E-ISSN 1867-5670, Vol. 37, p. 383-391Article in journal (Refereed)
    Abstract [en]

    We present a method for evaluating the discriminative power of compact feature combinations (blocks) using the distance-based scoring measure, yielding an algorithm for selecting feature blocks that significantly contribute to the outcome variation. To estimate classification performance with subset selection in a high dimensional framework we jointly evaluate both stages of the process: selection of significantly relevant blocks and classification. Classification power and performance properties of the classifier with the proposed subset selection technique has been studied on several simulation models and confirms the benefit of this approach.

  • 18. Pavlenko, Tatjana
    et al.
    Hall, Mikael
    Rosen, Dietrich von
    Towards the optimal feature selection in high-dimensional bayesian network classifiers2004Report (Other academic)
    Abstract [en]

    Incorporating subset selection into a classification method often carries a number of advantages, especially when operating in the domain of high-dimensional features. In this paper, we focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the

    growing dimension asymptotics, meaning that the number of training examples is relatively small compared to the number of feature nodes. In order to ascertain which set of features is indeed relevant for a classification task, we introduce a distance-based scoring measure

    reflecting how well the set separates different classes. This score is then employed to feature selection, using the weighted form of BN classifier. The idea is to view weights as inclusion-exclusion factors which eliminates the sets of features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy for different a priori assumptions concerning the separation strength.

  • 19.
    Pavlenko, Tatjana
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Hall, Mikael
    von Rosen, D
    Andrushchenko, Z
    Towards the optimal feature selection in high-dimensional Bayesian network classifiers2004In: SOFT METHODOLOGY AND RANDOM INFORMATION SYSTEMS / [ed] LopezDiaz, M; Gil, MA; Grzegorzewski, P; Hryniewicz, O; Lawry, J, SPRINGER-VERLAG BERLIN , 2004, p. 613-620Conference paper (Refereed)
    Abstract [en]

    We focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the growing dimension asymptotics. We modify the weighted BN by introducing inclusion-exclusion factors which eliminate the features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy.

  • 20. Pavlenko, Tatjana
    et al.
    Rosen, Dietrich von
    Estimating the discriminant function when the number of variables is large1996Report (Other academic)
  • 21. Pavlenko, Tatjana
    et al.
    von Rosen, D.
    Effect of dimensionality on discrimination2001In: Statistics, ISSN 0233-1888, Vol. 35, no 3, p. 191-213Article in journal (Refereed)
    Abstract [en]

    Discrimination problems in a high-dimensional setting are considered. New results are concerned with the role of dimensionality in the performance of the discrimination procedure. Assuming that data consist of a block structurc two different asymptotic approaches are presented. These approaches are characterized by different types of relations between the dimensionality and the size of the training samples.par Asymptotic expressions for the error probabilities are obtained and a consistent approximation of the discriminant function is proposed. Throughout the paper the importance of the dimensionality in the asymptotic analysis is stressed.

  • 22. Pavlenko, Tatjana
    et al.
    von Rosen, Dietrich
    On the optimal weighting of high-dimensional Bayesian networks2003Report (Other academic)
1 - 22 of 22
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