Browsing by Author "Semerci, Murat."

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    Discriminant ensembles and error analysis of classifier fusion rules
    (Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007., 2007.) Semerci, Murat.; Alpaydın, Ethem.
    Each classification algorithm has its own underlying assumption and misclassifies different patterns and overall accuracy can be increased by a suitable fusion of multiple classifiers. The combination is performed over the scores of classifiers, which are mostly posterior probabilities. Although the aim of classifier fusion is improved accuracy, there is no guarantee that this will be the case. In this study, we propose a new combination scheme which uses a subset of the classifier scores instead of using all of them. We experiment with three different methods for discriminant selection and combination, using decision trees and feature selection. We see that decision trees are better in choosing the best subset of features and accuracy is improved especially when the chosen discriminant outputs are combined with a trained linear model. In trying to understand the behavior of the fixed rules, we apply the idea of decomposing a loss function into bias, variance and noise. This study gives a brief survey of the bias, variance and noise decompositions in the literature for squared and 0/1 loss. We show that they are unable to explain the error behaviour of fusion rules, especially for minimum and maximum rules. We give the reasons why some fusion strategies work better than others under the assumptions of uniform or Gaussian noise. We propose instead a measure based on the area of intersection to explain the behavior of the fixed rules.
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    Distance approximations between high and multi-dimensional structures
    (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2019., 2019.) Semerci, Murat.; Cemgil, Ali Taylan.
    In this thesis, we focus on distance approximation methods between high and multi-dimensional structures and their applications. Two novel methods using distance approximations are proposed and they are applied to anomaly detection in cyber security (Distributed Denial of Service -DDoS- attack and attacker detection) and tensor decomposition in object retrieval (image and video classi cation on scarce data). At rst, we consider an autonomous cyber security system that consists of two components: A monitor for detection of DDoS attacks and a discriminator for detection of users in the system with malicious intents. A novel adaptive real time change-point detection model that tracks the changes in the Mahalanobis distances between sampled feature vectors in the monitored system accounts for possible DDoS attacks. A clustering model that runs over the similarity scores of behavioral patterns between the users is used for segregating the malicious from the innocent. Secondly, we propose a discriminative tensor decomposition with large margin (LMTD), which is a distance based model that nds the projection directions where the nearest neighbor classi - cation accuracy is improved over the projected instances. We experiment the cyber security system in a simulated SIP communication environment. Both the attack and attacker detection components are compared with some competitors in the literature. The tensor decomposition is applied to the image and video retrieval problem, where the data is scarce, and its performance also is compared with other decomposition methods. The experimental results are reported for both applications. It is shown that the proposed methods perform higher accuracy rates than their competitors.