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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperSubject: search.filters.subject.Feature ExtractionAuthor: search.filters.author.Kursun, OlcayAuthor: search.filters.author.Gürgen, Fïkret S.

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    Prediction of protein sub-nuclear location by clustering mRMR ensemble feature selection
    (2010) Sakar, C. Okan; Kursun, Olcay; Şeker, Hüseyin; Gürgen, Fïkret S.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Şeker, Hüseyin, Department of Informatics, De Montfort University, Leicester, United Kingdom; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, Turkey
    In many applications of pattern recognition in the bioinformatics and biomedical fields, input variables are organized into natural partitions that are called views in the literature. Mutual information can be used in selecting a minimal yet capable subset of views. Ignoring the presence of views, dismantling them, and treating their variables intermixed along with those of others at best results in a complex uninterpretable predictive system for researchers in these fields. Moreover, it would require measuring or computing majority of the views. We use the clustering indices of the views and rank the views according to the unique information they have with the target using minimum redundancy-maximum relevance (mRMR) approach. We also propose an ensemble approach to reduce the random variations in clusterings. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.
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    Feature extraction based on discriminative alternating regression
    (Springer Verlag [email protected], 2014) Sakar, C. Okan; Kursun, Olcay; Gürgen, Fïkret S.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, Turkey
    Canonical Correlation Analysis (CCA) aims at measuring linear relationships between two sets of variables (views). Recently, CCA has been used for feature extraction in classification problems with multiview data by means of view fusion. However, the extracted correlated features with CCA may not be discriminative since CCA does not utilize the class labels in its traditional formulation. Besides, the CCA features are computed based on within-set and between-set sample covariance matrices of the views which can be very sensitive to representation-specific details and noisy samples of the two views. In this paper, we propose a method, D-AR (Discriminative Alternating Regression), in which the two above-mentioned problems encountered in the application of CCA for feature extraction are addressed: (1) the class labels are incorporated into the proposed feature fusion framework to explore correlated and also discriminative features, and (2) the use of sensitive sample covariates matrices is avoided while fusing the two views. D-AR is a supervised feature fusion approach based on Multi-layer Perceptron (MLP) implementation of alternating regression. From the neurobiological perspective, the architecture of D-AR is similar to the model of a single neuron in the cerebral cortex which has a function of discovering and representing one of the hidden factors in its sensory environment. The MLP trained on each view aims to predict the class labels and also the hidden factors which are responsible for the correlation. We show that the features found by D-AR on training sets accomplishes significantly higher classification accuracies on test set of an experimental dataset. © Springer International Publishing Switzerland 2014. © 2017 Elsevier B.V., All rights reserved.