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BETA
Boeva, Veselka, ProfessorORCID iD iconorcid.org/0000-0003-3128-191x
Alternative names
Publications (10 of 19) Show all publications
Boeva, V., Angelova, M. & Tsiporkova, E. (2019). A split-merge evolutionary clustering algorithm. In: ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence: . Paper presented at 11th International Conference on Agents and Artificial Intelligence, ICAART; Prague, 19 February 2019 through 21 February 2019 (pp. 337-346). SciTePress, 2
Open this publication in new window or tab >>A split-merge evolutionary clustering algorithm
2019 (English)In: ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence, SciTePress , 2019, Vol. 2, p. 337-346Conference paper, Published paper (Refereed)
Abstract [en]

In this article we propose a bipartite correlation clustering technique that can be used to adapt the existing clustering solution to a clustering of newly collected data elements. The proposed technique is supposed to provide the flexibility to compute clusters on a new portion of data collected over a defined time period and to update the existing clustering solution by the computed new one. Such an updating clustering should better reflect the current characteristics of the data by being able to examine clusters occurring in the considered time period and eventually capture interesting trends in the area. For example, some clusters will be updated by merging with ones from newly constructed clustering while others will be transformed by splitting their elements among several new clusters. The proposed clustering algorithm, entitled Split-Merge Evolutionary Clustering, is evaluated and compared to another bipartite correlation clustering technique (PivotBiCluster) on two different case studies: expertise retrieval and patient profiling in healthcare. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Bipartite Clustering, Data Mining, Evolutionary Clustering, PubMed Data, Unsupervised Learning, Artificial intelligence, Cluster analysis, Evolutionary algorithms, Bipartite correlation clustering, Case-studies, Clustering solutions, Current characteristic, Data elements, Clustering algorithms
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17896 (URN)2-s2.0-85064827857 (Scopus ID)9789897583506 (ISBN)
Conference
11th International Conference on Agents and Artificial Intelligence, ICAART; Prague, 19 February 2019 through 21 February 2019
Available from: 2019-05-21 Created: 2019-05-21 Last updated: 2019-05-21Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2019). An Expertise Recommender System based on Data from an Institutional Repository (DiVA). In: Leslie Chan, Pierre Mounier (Ed.), Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers (pp. 135-149). OpenEdition Press
Open this publication in new window or tab >>An Expertise Recommender System based on Data from an Institutional Repository (DiVA)
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2019 (English)In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

Place, publisher, year, edition, pages
OpenEdition Press, 2019
Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-18095 (URN)979-1-0365-3801-8 (ISBN)979-1-0365-3802-5 (ISBN)
Note

open access

Available from: 2019-06-18 Created: 2019-06-18 Last updated: 2019-06-19Bibliographically approved
Boeva, V., Angelova, M., Angelova, M., Vishnu Manasa, D. & Tsiporkova, E. (2019). Bipartite Split-Merge Evolutionary Clustering. In: Lect. Notes Comput. Sci.: . Paper presented at 11th International Conference on Agents and Artificial Intelligence, ICAART; Prague; Czech Republic; 19 February 2019 through 21 February (pp. 204-223). Springer
Open this publication in new window or tab >>Bipartite Split-Merge Evolutionary Clustering
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2019 (English)In: Lect. Notes Comput. Sci., Springer , 2019, p. 204-223Conference paper, Published paper (Refereed)
Abstract [en]

We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge). © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Bipartite clustering, Data mining, Dynamic clustering, Evolutionary clustering, Split-merge framework, Unsupervised learning, Artificial intelligence, Bipartite correlation clustering, Clustering solutions, Clustering techniques, State-of-the-art algorithms, Cluster analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19127 (URN)10.1007/978-3-030-37494-5_11 (DOI)2-s2.0-85077496461 (Scopus ID)9783030374938 (ISBN)
Conference
11th International Conference on Agents and Artificial Intelligence, ICAART; Prague; Czech Republic; 19 February 2019 through 21 February
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-23Bibliographically approved
Lundberg, L., Lennerstad, H., Boeva, V. & García Martín, E. (2019). Handling non-linear relations in support vector machines through hyperplane folding. In: ACM International Conference Proceeding Series: . Paper presented at 11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February (pp. 137-141). Association for Computing Machinery
Open this publication in new window or tab >>Handling non-linear relations in support vector machines through hyperplane folding
2019 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2019, p. 137-141Conference paper, Published paper (Refereed)
Abstract [en]

We present a new method, called hyperplane folding, that increases the margin in Support Vector Machines (SVMs). Based on the location of the support vectors, the method splits the dataset into two parts, rotates one part of the dataset and then merges the two parts again. This procedure increases the margin as long as the margin is smaller than half of the shortest distance between any pair of data points from the two different classes. We provide an algorithm for the general case with n-dimensional data points. A small experiment with three folding iterations on 3-dimensional data points with non-linear relations shows that the margin does indeed increase and that the accuracy improves with a larger margin. The method can use any standard SVM implementation plus some basic manipulation of the data points, i.e., splitting, rotating and merging. Hyperplane folding also increases the interpretability of the data. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2019
Keywords
Hyperplane folding, Hyperplane hinging, Non-linear relations, Piecewise linear classification, Support vector machines, Geometry, Piecewise linear techniques, Vectors, Different class, Interpretability, Nonlinear relations, Piecewise linear, Support vector, Support vector machine (SVMs)
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18039 (URN)10.1145/3318299.3318319 (DOI)000477981500023 ()2-s2.0-85066460409 (Scopus ID)
Conference
11th International Conference on Machine Learning and Computing, ICMLC 2019; Zhuhai; China; 22 February 2019 through 24 February
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-09-10Bibliographically approved
Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H. & Lavesson, N. (2019). Higher order mining for monitoring district heating substations. In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019: . Paper presented at 6th IEEE International Conference on Data Science and Advanced Analytics, DSAA, Washington DC, 5 October 2019 through 8 October 2019 (pp. 382-391). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Higher order mining for monitoring district heating substations
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2019 (English)In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 382-391Conference paper, Published paper (Refereed)
Abstract [en]

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Clustering Analysis, Data Mining, District Heating Substations, Fault Detection, Higher Order Mining, Minimum Spanning Tree, Outlier Detection, Advanced Analytics, Anomaly detection, Clustering algorithms, Data visualization, District heating, Fault tree analysis, Fiber optics, Trees (mathematics), Consensus clustering, Data analysis techniques, Heating substations, Higher-order, Minimum spanning trees, Sequential-pattern mining, Visualization technique, Cluster analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-19237 (URN)10.1109/DSAA.2019.00053 (DOI)2-s2.0-85079289447 (Scopus ID)9781728144931 (ISBN)
Conference
6th IEEE International Conference on Data Science and Advanced Analytics, DSAA, Washington DC, 5 October 2019 through 8 October 2019
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2019). How to Measure Energy Consumption in Machine Learning Algorithms. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018 (pp. 243-255). , 11329
Open this publication in new window or tab >>How to Measure Energy Consumption in Machine Learning Algorithms
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2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, Vol. 11329, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11329
Keywords
Computer architecture, Energy efficiency, Green computing, Machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17209 (URN)10.1007/978-3-030-13453-2_20 (DOI)9783030134525 (ISBN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2018; Dublin; Ireland; 10 September 2018 through 14 September 2018
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-18Bibliographically approved
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2019). Monitoring Household Electricity Consumption Behaviour for Mining Changes. In: : . Paper presented at 3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China..
Open this publication in new window or tab >>Monitoring Household Electricity Consumption Behaviour for Mining Changes
2019 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18651 (URN)
Conference
3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China.
Projects
Scalable resource-efficient systems for big data analytics
Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-10-17Bibliographically approved
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2019). Profiling of household residents’ electricity consumption behavior using clustering analysis. In: Lect. Notes Comput. Sci.: . Paper presented at International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019 (pp. 779-786). Springer Verlag
Open this publication in new window or tab >>Profiling of household residents’ electricity consumption behavior using clustering analysis
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper, Published paper (Refereed)
Abstract [en]

In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Lecture Notes in Computer Science ; 11540
Keywords
Ambient Assisted Living, Non-intrusive remote monitoring, Assisted living, Clustering analysis, Clustering techniques, Electricity-consumption, Household level, Number of clusters, Remote monitoring, Electric power utilization
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18593 (URN)10.1007/978-3-030-22750-0_78 (DOI)2-s2.0-85068459816 (Scopus ID)9783030227494 (ISBN)
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-17Bibliographically approved
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2018). An Expertise Recommender System Based on Data from an Institutional Repository (DiVA). In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing: . Paper presented at 22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto.
Open this publication in new window or tab >>An Expertise Recommender System Based on Data from an Institutional Repository (DiVA)
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2018 (English)In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

Keywords
Text mining, Recommender system, Institutional repository, Ontology
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-16660 (URN)0.4000/proceedings.elpub.2018.17 (DOI)
Conference
22nd edition of the International Conference on ELectronic PUBlishing - Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure, Toronto
Note

open access

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2019-06-18Bibliographically approved
Boeva, V., Lundberg, L., Angelova, M. & Kohstall, J. (2018). Cluster Validation Measures for Label Noise Filtering. In: JardimGoncalves, R; Mendonca, JP; Jotsov, V; Marques, M; Martins, J; Bierwolf, R (Ed.), 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings: . Paper presented at 9th International Conference on Intelligent Systems, IS 2018; Funchal - Madeira; Portugal; 25 September 2018 through 27 (pp. 109-116). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Cluster Validation Measures for Label Noise Filtering
2018 (English)In: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings / [ed] JardimGoncalves, R; Mendonca, JP; Jotsov, V; Marques, M; Martins, J; Bierwolf, R, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 109-116Conference paper, Published paper (Refereed)
Abstract [en]

Cluster validation measures are designed to find the partitioning that best fits the underlying data. In this paper, we show that these well-known and scientifically proven validation measures can also be used in a different context, i.e., for filtering mislabeled instances or class outliers prior to training in super-vised learning problems. A technique, entitled CVI-based Outlier Filtering, is proposed in which mislabeled instances are identified and eliminated from the training set, and a classification hypothesis is then built from the set of remaining instances. The proposed approach assigns each instance several cluster validation scores representing its potential of being an outlier with respect to the clustering properties the used validation measures assess. We examine CVI-based Outlier Filtering and compare it against the LOF detection method on ten data sets from the UCI data repository using five well-known learning algorithms and three different cluster validation indices. In addition, we study two approaches for filtering mislabeled instances: local and global. Our results show that for most learning algorithms and data sets, the proposed CVI-based outlier filtering algorithm outperforms the baseline method (LOF). The greatest increase in classification accuracy has been achieved by combining at least two of the used cluster validation indices and global filtering of mislabeled instances. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
Class noise, Classification, Cluster validation measures, Label noise, Classification (of information), Intelligent systems, Learning algorithms, Statistics, Classification accuracy, Cluster validation, Clustering properties, Data repositories, Detection methods, Filtering algorithm, Learning problem, Clustering algorithms
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18023 (URN)10.1109/IS.2018.8710495 (DOI)000469337900017 ()2-s2.0-85065973083 (Scopus ID)9781538670972 (ISBN)
Conference
9th International Conference on Intelligent Systems, IS 2018; Funchal - Madeira; Portugal; 25 September 2018 through 27
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-07-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3128-191x

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