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Halas, R., Mesiar, R., Pocs, J. & Torra, V. (2019). A note on some algebraic properties of discrete Sugeno integrals. Fuzzy sets and systems (Print), 355, 110-120
Open this publication in new window or tab >>A note on some algebraic properties of discrete Sugeno integrals
2019 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 355, p. 110-120Article in journal (Refereed) Published
Abstract [en]

Based on the link between Sugeno integrals and fuzzy measures, we discuss several algebraic properties of discrete Sugeno integrals. We recall that the composition of Sugeno integrals is again a Sugeno integral, and that each Sugeno integral can be obtained as a composition of binary Sugeno integrals. In particular, we discuss the associativity, dominance, commuting and bisymmetry of Sugeno integrals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Associativity, L-measure, Sugeno integral
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16471 (URN)10.1016/j.fss.2018.01.009 (DOI)000450287700009 ()2-s2.0-85041636724 (Scopus ID)
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-14Bibliographically approved
Senavirathne, N. & Torra, V. (2019). Integrally private model selection for decision trees. Computers & security (Print), 83, 167-181
Open this publication in new window or tab >>Integrally private model selection for decision trees
2019 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 83, p. 167-181Article in journal (Refereed) Published
Abstract [en]

Privacy attacks targeting machine learning models are evolving. One of the primary goals of such attacks is to infer information about the training data used to construct the models. “Integral Privacy” focuses on machine learning and statistical models which explain how we can utilize intruder's uncertainty to provide a privacy guarantee against model comparison attacks. Through experimental results, we show how the distribution of models can be used to achieve integral privacy. Here, we observe two categories of machine learning models based on their frequency of occurrence in the model space. Then we explain the privacy implications of selecting each of them based on a new attack model and empirical results. Also, we provide recommendations for private model selection based on the accuracy and stability of the models along with the diversity of training data that can be used to generate the models. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Data privacy, Integral privacy, Machine learning model space, Privacy models, Privacy preserving machine learning, Decision trees, Attack model, Machine learning models, Model comparison, Model Selection, Privacy Attacks, Privacy preserving, Training data, Machine learning
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16682 (URN)10.1016/j.cose.2019.01.006 (DOI)2-s2.0-85062062700 (Scopus ID)
Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-03-08
Torra, V. & Senavirathne, N. (2019). Maximal c consensus meets. Information Fusion, 51, 58-66
Open this publication in new window or tab >>Maximal c consensus meets
2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, p. 58-66Article in journal (Refereed) Published
Abstract [en]

Given a set S of subsets of a reference set X, we define the problem of finding c subsets of X that maximize the size of the intersection among the included subsets. Maximizing the size of the intersection means that they are subsets of the sets in S and they are as large as possible. We can understand the result of this problem as c consensus sets of S, or c consensus representatives of S. From the perspective of lattice theory, each representative will be a meet of some sets in S. In this paper we define formally this problem, and present heuristic algorithms to solve it. We also discuss the relationship with other established problems in the literature.

Place, publisher, year, edition, pages
Elsevier B.V., 2019
Keywords
clustering, consensus clustering, heuristic algorithms, Maximal c consensus meets, Cluster analysis, Clustering algorithms, Lattice theory, Set theory, Reference set
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16463 (URN)10.1016/j.inffus.2018.09.011 (DOI)2-s2.0-85056612105 (Scopus ID)
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-03-11
Koloseni, D., Helldin, T. & Torra, V. (2018). Absolute and relative preferences in AHP-like matrices. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018). Paper presented at Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018 (pp. 260-267). World Scientific Publishing Co. Pte. Ltd., 11
Open this publication in new window or tab >>Absolute and relative preferences in AHP-like matrices
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, World Scientific Publishing Co. Pte. Ltd. , 2018, Vol. 11, p. 260-267Conference paper, Published paper (Refereed)
Abstract [en]

The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

Place, publisher, year, edition, pages
World Scientific Publishing Co. Pte. Ltd., 2018
Series
World Scientific Proceedings Series on Computer Engineering and Information Science, ISSN 1793-7868 ; 11
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16409 (URN)10.1142/9789813273238_0035 (DOI)978-981-3273-22-1 (ISBN)978-981-3273-24-5 (ISBN)
Conference
Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018), Belfast, United Kingdom, August 21-24, 2018
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-08Bibliographically approved
Senavirathne, N. & Torra, V. (2018). Approximating Robust Linear Regression With An Integral Privacy Guarantee. In: Kieran McLaughlin, Ali Ghorbani, Sakir Sezer, Rongxing Lu, Liqun Chen, Robert H. Deng, Paul Miller, Stephen Marsh, Jason Nurse (Ed.), 2018 16th Annual Conference on Privacy, Security and Trust (PST): . Paper presented at 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Northern Ireland, August 28-30, 2018 (pp. 85-94). IEEE
Open this publication in new window or tab >>Approximating Robust Linear Regression With An Integral Privacy Guarantee
2018 (English)In: 2018 16th Annual Conference on Privacy, Security and Trust (PST) / [ed] Kieran McLaughlin, Ali Ghorbani, Sakir Sezer, Rongxing Lu, Liqun Chen, Robert H. Deng, Paul Miller, Stephen Marsh, Jason Nurse, IEEE, 2018, p. 85-94Conference paper, Published paper (Refereed)
Abstract [en]

Most of the privacy-preserving techniques suffer from an inevitable utility loss due to different perturbations carried out on the input data or the models in order to gain privacy. When it comes to machine learning (ML) based prediction models, accuracy is the key criterion for model selection. Thus, an accuracy loss due to privacy implementations is undesirable. The motivation of this work, is to implement the privacy model "integral privacy" and to evaluate its eligibility as a technique for machine learning model selection while preserving model utility. In this paper, a linear regression approximation method is implemented based on integral privacy which ensures high accuracy and robustness while maintaining a degree of privacy for ML models. The proposed method uses a re-sampling based estimator to construct linear regression model which is coupled with a rounding based data discretization method to support integral privacy principles. The implementation is evaluated in comparison with differential privacy in terms of privacy, accuracy and robustness of the output ML models. In comparison, integral privacy based solution provides a better solution with respect to the above criteria.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Annual Conference on Privacy Security and Trust-PST, ISSN 1712-364X
Keywords
Integral privacy, Linear regression, Privacy-preserving machine learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
urn:nbn:se:his:diva-16573 (URN)10.1109/PST.2018.8514161 (DOI)000454683600008 ()978-1-5386-7494-9 (ISBN)978-1-5386-7493-2 (ISBN)
Conference
16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Northern Ireland, August 28-30, 2018
Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-02-15Bibliographically approved
Torra, V., Guillen, M. & Santolino, M. (2018). Continuous m-dimensional distorted probabilities. Information Fusion, 44, 97-102
Open this publication in new window or tab >>Continuous m-dimensional distorted probabilities
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 44, p. 97-102Article in journal (Refereed) Published
National Category
Engineering and Technology Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14763 (URN)10.1016/j.inffus.2017.12.004 (DOI)000435059200008 ()2-s2.0-85039798077 (Scopus ID)
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2019-02-14Bibliographically approved
Alcantud, J. C. & Torra, V. (2018). Decomposition theorems and extension principles for hesitant fuzzy sets. Information Fusion, 41, 48-56
Open this publication in new window or tab >>Decomposition theorems and extension principles for hesitant fuzzy sets
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 41, p. 48-56Article in journal (Refereed) Published
Abstract [en]

We prove a decomposition theorem for hesitant fuzzy sets, which states that every typical hesitant fuzzy set on a set can be represented by a well-structured family of fuzzy sets on that set. This decomposition is expressed by the novel concept of hesitant fuzzy set associated with a family of hesitant fuzzy sets, in terms of newly defined families of their cuts. Our result supposes the first representation theorem of hesitant fuzzy sets in the literature. Other related representation results are proven. We also define two novel extension principles that extend crisp functions to functions that map hesitant fuzzy sets into hesitant fuzzy sets.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Hesitant fuzzy set, Cut set, Decomposition theorem, Representation theorem, Extension principle
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14605 (URN)10.1016/j.inffus.2017.08.005 (DOI)000417662100006 ()2-s2.0-85027534671 (Scopus ID)
Available from: 2017-12-28 Created: 2017-12-28 Last updated: 2019-02-14Bibliographically approved
Saleh, E., Valls, A., Moreno, A., Romero-Aroca, P., Torra, V. & Bustince, H. (2018). Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models. In: Vicenç Torra, Yasuo Narukawa, Isabel Aguiló, Manuel González-Hidalgo (Ed.), Modeling Decisions for Artificial Intelligence: 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018, Proceedings. Paper presented at International Conference on Modeling Decisions for Artificial Intelligence MDAI 2018, 15 October 2018 through 18 October 2018, Mallorca, Spain (pp. 114-127). Springer
Open this publication in new window or tab >>Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models
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2018 (English)In: Modeling Decisions for Artificial Intelligence: 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Isabel Aguiló, Manuel González-Hidalgo, Springer, 2018, p. 114-127Conference paper, Published paper (Refereed)
Abstract [en]

Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature. 

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11144
Keywords
Aggregation functions, Choquet integral, Diabetic retinopathy, Fuzzy measures, Fuzzy rule-based systems, Sugeno integral, Artificial intelligence, Decision support systems, Eye protection, Fuzzy rules, Integral equations, Risk perception, Sugeno integrals, Fuzzy inference
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16416 (URN)10.1007/978-3-030-00202-2_10 (DOI)2-s2.0-85055680053 (Scopus ID)978-3-030-00201-5 (ISBN)978-3-030-00202-2 (ISBN)
Conference
International Conference on Modeling Decisions for Artificial Intelligence MDAI 2018, 15 October 2018 through 18 October 2018, Mallorca, Spain
Note

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11144)

Available from: 2018-11-20 Created: 2018-11-20 Last updated: 2019-02-14Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M. & Torra, V. (2018). On the Visualization of Discrete Non-additive Measures. In: Torra V, Mesiar R, Baets B (Ed.), Aggregation Functions in Theory and in Practice AGOP 2017: . Paper presented at 9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017 (pp. 200-210). Springer
Open this publication in new window or tab >>On the Visualization of Discrete Non-additive Measures
2018 (English)In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper, Published paper (Refereed)
Abstract [en]

Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

Place, publisher, year, edition, pages
Springer, 2018
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 581
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15590 (URN)10.1007/978-3-319-59306-7_21 (DOI)000432811600021 ()2-s2.0-85019989762 (Scopus ID)978-3-319-59306-7 (ISBN)978-3-319-59305-0 (ISBN)
Conference
9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017
Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-10-02Bibliographically approved
Salas, J., Megías, D. & Torra, V. (2018). SwapMob: Swapping trajectories for mobility anonymization. In: Josep Domingo-Ferrer, Fransisco Montes (Ed.), Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings. Paper presented at International Conference on Privacy in Statistical Databases, PSD 2018, Valencia, Spain, September 26–28, 2018 (pp. 331-346). Springer
Open this publication in new window or tab >>SwapMob: Swapping trajectories for mobility anonymization
2018 (English)In: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings / [ed] Josep Domingo-Ferrer, Fransisco Montes, Springer, 2018, p. 331-346Conference paper, Published paper (Refereed)
Abstract [en]

Mobility data mining can improve decision making, from planning transports in metropolitan areas to localizing services in towns. However, unrestricted access to such data may reveal sensible locations and pose safety risks if the data is associated to a specific moving individual. This is one of the many reasons to consider trajectory anonymization. Some anonymization methods rely on grouping individual registers on a database and publishing summaries in such a way that individual information is protected inside the group. Other approaches consist of adding noise, such as differential privacy, in a way that the presence of an individual cannot be inferred from the data. In this paper, we present a perturbative anonymization method based on swapping segments for trajectory data (SwapMob). It preserves the aggregate information of the spatial database and at the same time, provides anonymity to the individuals. We have performed tests on a set of GPS trajectories of 10,357 taxis during the period of Feb. 2 to Feb. 8, 2008, within Beijing. We show that home addresses and POIs of specific individuals cannot be inferred after anonymizing them with SwapMob, and remark that the aggregate mobility data is preserved without changes, such as the average length of trajectories or the number of cars and their directions on any given zone at a specific time.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11126
Keywords
aggregates, data mining, database systems, decision making, trajectories, anonymization, average length, differential privacies, gps trajectories, metropolitan area, mobility datum, spatial database, trajectory data, data privacy
National Category
Transport Systems and Logistics Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
urn:nbn:se:his:diva-16303 (URN)10.1007/978-3-319-99771-1_22 (DOI)2-s2.0-85053904138 (Scopus ID)978-3-319-99770-4 (ISBN)978-3-319-99771-1 (ISBN)
Conference
International Conference on Privacy in Statistical Databases, PSD 2018, Valencia, Spain, September 26–28, 2018
Available from: 2018-10-16 Created: 2018-10-16 Last updated: 2019-02-14Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0368-8037

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