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Your search keyword '"Iglesias, E L"' showing total 39 results
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39 results on '"Iglesias, E L"'

1. To Enhance Full-Text Biomedical Document Classification Through Semantic Enrichment

Author

Gonçalves, C. A., Seara Vieira, A., Gonçalves, C. T., Borrajo, L., Camacho, R., Iglesias, E. L., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, García Bringas, Pablo, editor, Pérez García, Hilde, editor, Martínez de Pisón, Francisco Javier, editor, Martínez Álvarez, Francisco, editor, Troncoso Lora, Alicia, editor, Herrero, Álvaro, editor, Calvo Rolle, José Luis, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2023
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4. LearnSec: A Framework for Full Text Analysis

Author

Gonçalves, Carlos, Iglesias, E. L., Borrajo, L., Camacho, Rui, Seara Vieira, A., Gonçalves, Célia Talma, 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, de Cos Juez, Francisco Javier, editor, Villar, José Ramón, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Quintián, Héctor, editor, Sáez, José António, editor, and Corchado, Emilio, editor

Published
2018
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13. Tokenising, Stemming and Stopword Removal on Anti-spam Filtering Domain

Author

Méndez, J. R., Iglesias, E. L., Fdez-Riverola, F., Díaz, F., Corchado, J. M., 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Marín, Roque, editor, Onaindía, Eva, editor, Bugarín, Alberto, editor, and Santos, José, editor

Published
2006
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14. Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System

Author

Méndez, J. R., Fdez-Riverola, F., Iglesias, E. L., Díaz, F., Corchado, J. M., 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Roth-Berghofer, Thomas R., editor, Göker, Mehmet H., editor, and Güvenir, H. Altay, editor

Published
2006
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15. A Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain

Author

Méndez, J. R., Fdez-Riverola, F., Díaz, F., Iglesias, E. L., Corchado, J. M., 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, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Perner, Petra, editor

Published
2006
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16. Improving imbalanced scientific text classification using sampling strategies and dictionaries

Author

Borrajo L., Romero R., Iglesias E. L., and Marey C. M. Redondo

Subjects
Biotechnology, TP248.13-248.65
Abstract

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation.

Published
2011
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24. An HMM-based synthetic view generator to improve the efficiency of ensemble systems.

Author

Borrajo, L, Vieira, A Seara, and Iglesias, E L

Subjects
SUPERVISED learning, TEST systems, HIDDEN Markov models
Abstract

One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models (HMMs). To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach. [ABSTRACT FROM AUTHOR]

Published
2020
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26. Applying lazy learning algorithms to tackle concept drift in spam filtering

Author

Fernández Riverola, Florentino, Iglesias, E. L., Díaz, Fernando, Méndez, Jose R., and Corchado Rodríguez, Juan Manuel

Subjects
Computer Science
Abstract

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

Published
2007

27. SpamHunting: An instance-based reasoning system for spam labelling and filtering

Author

Fernández Riverola, Florentino, Iglesias, E. L., Díaz, Fernando, Méndez, Jose R., and Corchado Rodríguez, Juan Manuel

Subjects
Computer Science
Abstract

n this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable en-hanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the tar-get case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules.

Published
2007

29. A Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain.

Author

Perner, Petra, Méndez, J. R., Fdez-Riverola, F., Díaz, F., Iglesias, E. L., and Corchado, J. M.

Abstract

In this paper we analyse the strengths and weaknesses of the mainly used feature selection methods in text categorization when they are applied to the spam problem domain. Several experiments with different feature selection methods and content-based filtering techniques are carried out and discussed. Information Gain, χ2-text, Mutual Information and Document Frequency feature selection methods have been analysed in conjunction with Naïve Bayes, boosting trees, Support Vector Machines and ECUE models in different scenarios. From the experiments carried out the underlying ideas behind feature selection methods are identified and applied for improving the feature selection process of SpamHunting, a novel anti-spam filtering software able to accurate classify suspicious e-mails. [ABSTRACT FROM AUTHOR]

Published
2006
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30. Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System.

Author

Roth-Berghofer, Thomas R., Göker, Mehmet H., Güvenir, H. Altay, Méndez, J. R., Fdez-Riverola, F., Iglesias, E. L., Díaz, F., and Corchado, J. M.

Abstract

In this paper we propose a novel feature selection method able to handle concept drift problems in spam filtering domain. The proposed technique is applied to a previous successful instance-based reasoning e-mail filtering system called SpamHunting. Our achieved information criterion is based on several ideas extracted from the well-known information measure introduced by Shannon. We show how results obtained by our previous system in combination with the improved feature selection method outperforms classical machine learning techniques and other well-known lazy learning approaches. In order to evaluate the performance of all the analysed models, we employ two different corpus and six well-known metrics in various scenarios. [ABSTRACT FROM AUTHOR]

Published
2006
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31. Tokenising, Stemming and Stopword Removal on Anti-spam Filtering Domain.

Author

Marín, Roque, Onaindía, Eva, Bugarín, Alberto, Santos, José, Méndez, J. R., Iglesias, E. L., Fdez-Riverola, F., Díaz, F., and Corchado, J. M.

Abstract

Junk e-mail detection and filtering can be considered a cost-sensitive classification problem. Nevertheless, preprocessing methods and noise reduction strategies used to enhance the computational efficiency in text classification cannot be so efficient in e-mail filtering. This fact is demonstrated here where a comparative study of the use of stopword removal, stemming and different tokenising schemes is presented. The final goal is to preprocess the training e-mail corpora of several content-based techniques for spam filtering (machine approaches and case-based systems). Soundness conclusions are extracted from the experiments carried out where different scenarios are taken into consideration. [ABSTRACT FROM AUTHOR]

Published
2006
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34. Tokenising, stemming and stopword removal on anti-spam filtering domain

Author

Méndez, J. R., Iglesias, E. L., Fdez-Riverola, F., Díaz, F., and Juan Manuel Corchado

Subjects
Computer Science
Abstract

Junk e-mail detection and filtering can be considered a cost-sensitive classification problem. Nevertheless, preprocessing methods and noise reduction strategies used to enhance the computational efficiency in text classification cannot be so efficient in e-mail filtering. This fact is demonstrated here where a comparative study of the use of stopword removal, stemming and different tokenising schemes is presented. The final goal is to preprocess the training e-mail corpora of several content-based techniques for spam filtering (machine approaches and case-based systems). Soundness conclusions are extracted from the experiments carried out where different scenarios are taken into consideration.

37. Tracking concept drift at feature selection stage in SpamHunting: An anti-spam instance-based reasoning system

Author

Méndez, J. R., Fdez-Riverola, F., Iglesias, E. L., Díaz, F., and Juan Manuel Corchado

Subjects
Computer Science
Abstract

In this paper we propose a novel feature selection method able to handle concept drift problems in spam filtering domain. The proposed technique is applied to a previous successful instance-based reasoning e-mail filtering system called SpamHunting. Our achieved information criterion is based on several ideas extracted from the well-known information measure introduced by Shannon. We show how results obtained by our previous system in combination with the improved feature selection method outperforms classical machine learning techniques and other well-known lazy learning approaches. In order to evaluate the performance of all the analysed models, we employ two different corpus and six well-known metrics in various scenarios.

38. Improving the text classification using clustering and a novel HMM to reduce the dimensionality.

Author

Seara Vieira A, Borrajo L, and Iglesias EL

Subjects
Markov Chains, Cluster Analysis, Models, Theoretical
Abstract

In text classification problems, the representation of a document has a strong impact on the performance of learning systems. The high dimensionality of the classical structured representations can lead to burdensome computations due to the great size of real-world data. Consequently, there is a need for reducing the quantity of handled information to improve the classification process. In this paper, we propose a method to reduce the dimensionality of a classical text representation based on a clustering technique to group documents, and a previously developed Hidden Markov Model to represent them. We have applied tests with the k-NN and SVM classifiers on the OHSUMED and TREC benchmark text corpora using the proposed dimensionality reduction technique. The experimental results obtained are very satisfactory compared to commonly used techniques like InfoGain and the statistical tests performed demonstrate the suitability of the proposed technique for the preprocessing step in a text classification task., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Published
2016
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39. Study of query expansion techniques and their application in the biomedical information retrieval.

Author

Rivas AR, Iglesias EL, and Borrajo L

Subjects
Algorithms, Data Mining methods, Database Management Systems, Databases, Factual, MEDLINE, Natural Language Processing, User-Computer Interface
Abstract

Information Retrieval focuses on finding documents whose content matches with a user query from a large document collection. As formulating well-designed queries is difficult for most users, it is necessary to use query expansion to retrieve relevant information. Query expansion techniques are widely applied for improving the efficiency of the textual information retrieval systems. These techniques help to overcome vocabulary mismatch issues by expanding the original query with additional relevant terms and reweighting the terms in the expanded query. In this paper, different text preprocessing and query expansion approaches are combined to improve the documents initially retrieved by a query in a scientific documental database. A corpus belonging to MEDLINE, called Cystic Fibrosis, is used as a knowledge source. Experimental results show that the proposed combinations of techniques greatly enhance the efficiency obtained by traditional queries.

Published
2014
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