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108 results on '"Ougiaroglou, Stefanos"'

2. An Empirical Analysis of Data Reduction Techniques for k-NN Classification

Author

Eleftheriadis, Stylianos, Evangelidis, Georgios, Ougiaroglou, Stefanos, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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4. A Web Tool for K-means Clustering

Author

Gratsos, Konstantinos, Ougiaroglou, Stefanos, Margaris, Dionisis, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabassi, Katerina, editor, Mylonas, Phivos, editor, and Caro, Jaime, editor

Published
2023
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7. Prototype Generation for Multi-label Nearest Neighbours Classification

Author

Ougiaroglou, Stefanos, Filippakis, Panagiotis, Evangelidis, Georgios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sanjurjo González, Hugo, editor, Pastor López, Iker, editor, García Bringas, Pablo, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2021
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8. Dynamic k-NN Classification Based on Region Homogeneity

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, Diamantaras, Konstantinos I., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Darmont, Jérôme, editor, Novikov, Boris, editor, and Wrembel, Robert, editor

Published
2020
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9. WebApriori: A Web Application for Association Rules Mining

Author

Malliaridis, Konstantinos, Ougiaroglou, Stefanos, Dervos, Dimitris A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kumar, Vivekanandan, editor, and Troussas, Christos, editor

Published
2020
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10. Knowledge Graphs and Semantic Web Tools in Cyber Threat Intelligence: A Systematic Literature Review.

Author

Bratsas, Charalampos, Anastasiadis, Efstathios Konstantinos, Angelidis, Alexandros K., Ioannidis, Lazaros, Kotsakis, Rigas, and Ougiaroglou, Stefanos

Subjects
KNOWLEDGE graphs, SEMANTIC Web, COMPUTER crimes, DATA mining, INTERNET security
Abstract

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Fast Tree-Based Classification via Homogeneous Clustering

Author

Pardis, George, Diamantaras, Konstantinos I., Ougiaroglou, Stefanos, Evangelidis, Georgios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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12. Improving Data Reduction by Merging Prototypes

Author

Ponos, Pavlos, Ougiaroglou, Stefanos, Evangelidis, Georgios, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Welzer, Tatjana, editor, Eder, Johann, editor, Podgorelec, Vili, editor, and Kamišalić Latifić, Aida, editor

Published
2019
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13. An Extensive Experimental Study on the Cluster-based Reference Set Reduction for speeding-up the k-NN Classifier

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, and Dervos, Dimitris A.

Subjects
Computer Science - Learning
Abstract

The k-Nearest Neighbor (k-NN) classification algorithm is one of the most widely-used lazy classifiers because of its simplicity and ease of implementation. It is considered to be an effective classifier and has many applications. However, its major drawback is that when sequential search is used to find the neighbors, it involves high computational cost. Speeding-up k-NN search is still an active research field. Hwang and Cho have recently proposed an adaptive cluster-based method for fast Nearest Neighbor searching. The effectiveness of this method is based on the adjustment of three parameters. However, the authors evaluated their method by setting specific parameter values and using only one dataset. In this paper, an extensive experimental study of this method is presented. The results, which are based on five real life datasets, illustrate that if the parameters of the method are carefully defined, one can achieve even better classification performance., Comment: Proceeding of International Conference on Integrated Information (IC-InInfo 2011), pp. 12-15, Kos island, Greece, 2011

Published
2013

14. Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and Citations.

Author

Kampatzis, Aristotelis, Sidiropoulos, Antonis, Diamantaras, Konstantinos, and Ougiaroglou, Stefanos

Subjects
SENTIMENT analysis, NATURAL language processing, SCIENTIFIC communication, CLASSIFICATION, MACHINE learning, INTENTION
Abstract

Sentiment Analysis in text, especially text containing scientific citations, is an emerging research field with important applications in the research community. This review explores the field of sentiment analysis by focusing on the interpretation of citations, presenting a detailed description of techniques and methods ranging from lexicon-based approaches to Machine and Deep Learning models. The importance of understanding both the emotion and the intention behind citations is emphasized, reflecting their critical role in scientific communication. In addition, this study presents the challenges faced by researchers (such as complex scientific terminology, multilingualism, and the abstract nature of scientific discourse), highlighting the need for specialized language processing techniques. Finally, future research directions include improving the quality of datasets as well as exploring architectures and models to improve the accuracy of sentiment detection. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Generating Fixed-Size Training Sets for Large and Streaming Datasets

Author

Ougiaroglou, Stefanos, Arampatzis, Georgios, Dervos, Dimitris A., Evangelidis, Georgios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kirikova, Mārīte, editor, Nørvåg, Kjetil, editor, and Papadopoulos, George A., editor

Published
2017
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19. Efficient Support Vector Machine Classification Using Prototype Selection and Generation

Author

Ougiaroglou, Stefanos, Diamantaras, Konstantinos I., Evangelidis, Georgios, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Iliadis, Lazaros, editor, and Maglogiannis, Ilias, editor

Published
2016
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25. WebDR: A Web Workbench for Data Reduction

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Calders, Toon, editor, Esposito, Floriana, editor, Hüllermeier, Eyke, editor, and Meo, Rosa, editor

Published
2014
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26. EHC: Non-parametric Editing by Finding Homogeneous Clusters

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Beierle, Christoph, editor, and Meghini, Carlo, editor

Published
2014
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27. Prototype Selection for Multilabel Instance-Based Learning †.

Author

Filippakis, Panagiotis, Ougiaroglou, Stefanos, and Evangelidis, Georgios

Subjects
*DATA reduction, *PROTOTYPES, *SUPERVISED learning
Abstract

Reducing the size of the training set, which involves replacing it with a condensed set, is a widely adopted practice to enhance the efficiency of instance-based classifiers while trying to maintain high classification accuracy. This objective can be achieved through the use of data reduction techniques, also known as prototype selection or generation algorithms. Although there are numerous algorithms available in the literature that effectively address single-label classification problems, most of them are not applicable to multilabel data, where an instance can belong to multiple classes. Well-known transformation methods cannot be combined with a data reduction technique due to different reasons. The Condensed Nearest Neighbor rule is a popular parameter-free single-label prototype selection algorithm. The IB2 algorithm is the one-pass variation of the Condensed Nearest Neighbor rule. This paper proposes variations of these algorithms for multilabel data. Through an experimental study conducted on nine distinct datasets as well as statistical tests, we demonstrate that the eight proposed approaches (four for each algorithm) offer significant reduction rates without compromising the classification accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Applying General-Purpose Data Reduction Techniques for Fast Time Series Classification

Author

Ougiaroglou, Stefanos, Karamitopoulos, Leonidas, Tatoglou, Christos, Evangelidis, Georgios, Dervos, Dimitris A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mladenov, Valeri, editor, Koprinkova-Hristova, Petia, editor, Palm, Günther, editor, Villa, Alessandro E. P., editor, Appollini, Bruno, editor, and Kasabov, Nikola, editor

Published
2013
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31. A Simple Noise-Tolerant Abstraction Algorithm for Fast k-NN Classification

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Corchado, Emilio, editor, Snášel, Václav, editor, Abraham, Ajith, editor, Woźniak, Michał, editor, Graña, Manuel, editor, and Cho, Sung-Bae, editor

Published
2012
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32. An Adaptive Hybrid and Cluster-Based Model for Speeding Up the k-NN Classifier

Author

Ougiaroglou, Stefanos, Evangelidis, Georgios, Dervos, Dimitris A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Corchado, Emilio, editor, Snášel, Václav, editor, Abraham, Ajith, editor, Woźniak, Michał, editor, Graña, Manuel, editor, and Cho, Sung-Bae, editor

Published
2012
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33. Occupational Guidance through ELECTRE Method

Author

Ougiaroglou, Stefanos, Kazanidis, Ioannis, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Leung, Howard, editor, Popescu, Elvira, editor, Cao, Yiwei, editor, Lau, Rynson W. H., editor, and Nejdl, Wolfgang, editor

Published
2011
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36. Adaptive k-Nearest-Neighbor Classification Using a Dynamic Number of Nearest Neighbors

Author

Ougiaroglou, Stefanos, Nanopoulos, Alexandros, Papadopoulos, Apostolos N., Manolopoulos, Yannis, Welzer-Druzovec, Tatjana, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ioannidis, Yannis, editor, Novikov, Boris, editor, and Rachev, Boris, editor

Published
2007
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40. Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets.

Author

Margaris, Dionisis, Vassilakis, Costas, Spiliotopoulos, Dimitris, and Ougiaroglou, Stefanos

Subjects
RECOMMENDER systems, FILTERS & filtration, RELIABILITY in engineering, FORECASTING, COMPUTER systems
Abstract

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered "close" to the user for whom the prediction is being generated have given to the same product or service. In general, "close" users for some user u correspond to users that have rated items similarly to u and these users are termed as "near neighbors". As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset's density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method's efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets. [ABSTRACT FROM AUTHOR]

Published
2023
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