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Boeva, Veselka, ProfessorORCID iD iconorcid.org/0000-0003-3128-191x
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Publications (9 of 9) Show all publications
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
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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
Angelova, M., Vishnu Manasa, D., Boeva, V., Linde, P. & Lavesson, N. (2018). An Expertise Recommender SystemBased on Data from an InstitutionalRepository (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 SystemBased on Data from an InstitutionalRepository (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: 2018-06-29Bibliographically approved
Boeva, V., Angelova, M., Lavesson, N., Rosander, O. & Tsiporkova, E. (2018). Evolutionary clustering techniques for expertise mining scenarios. In: van den Herik J.,Rocha A.P. (Ed.), ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2: . Paper presented at 10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira (pp. 523-530). SciTePress, 2
Open this publication in new window or tab >>Evolutionary clustering techniques for expertise mining scenarios
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2018 (English)In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress , 2018, Vol. 2, p. 523-530Conference paper, Published paper (Refereed)
Abstract [en]

The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Place, publisher, year, edition, pages
SciTePress, 2018
Keywords
Data Mining, Expert Finding, Health Science, Knowledge Management, Natural Language Processing, Artificial intelligence, Cluster analysis, Natural language processing systems, Search engines, Clustering approach, Clustering solutions, Data elements, Evolutionary clustering, Retrieval systems, System database
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16224 (URN)2-s2.0-85046663632 (Scopus ID)9789897582752 (ISBN)
Conference
10th International Conference on Agents and Artificial Intelligence, ICAART, Funchal, Madeira
Available from: 2018-05-24 Created: 2018-05-24 Last updated: 2018-05-24Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. (2018). Hoeffding Trees with nmin adaptation. In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018): . Paper presented at 5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin (pp. 70-79). IEEE
Open this publication in new window or tab >>Hoeffding Trees with nmin adaptation
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2018 (English)In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
Keywords
data stream mining; green artificial intelligence; energy efficiency; hoeffding trees; energy aware machine learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17207 (URN)10.1109/DSAA.2018.00017 (DOI)000459238600008 ()978-1-5386-5090-5 (ISBN)
Conference
5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin
Funder
Knowledge Foundation, 20140032
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2019-04-05Bibliographically approved
Nordahl, C., Grahn, H., Persson, M. & Boeva, V. (2018). Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.. In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis: . Paper presented at 2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm. https://sites.google.com/view/arial2018/accepted-papersprogram
Open this publication in new window or tab >>Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.
2018 (English)In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Conference paper, Published paper (Refereed)
Abstract [en]

We propose a cluster analysis approach for organizing, visualizing and understanding households’ electricity consumption data. We initially partition the consumption data into a number of clusters with similar daily electricity consumption profiles. The centroids of each cluster can be seen as representative signatures of a household’s electricity consumption behaviors. We evaluate the proposed approach by conducting a number of experiments on electricity consumption data of ten selected households. Our results show that the approach is suitable for data analysis, understanding and creating electricity consumption behavior models.

Place, publisher, year, edition, pages
https://sites.google.com/view/arial2018/accepted-papersprogram, 2018
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:bth-17439 (URN)
Conference
2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm
Projects
BigData@BTH
Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2019-01-16Bibliographically approved
Boeva, V., Lundberg, L., Kota, S. M. H. & Sköld, L. (2017). Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level. In: Gottumukkala, R Ning, X Dong, G Raghavan, V Aluru, S Karypis, G Miele, L Wu, X (Ed.), 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017): . Paper presented at 17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA (pp. 170-176). IEEE
Open this publication in new window or tab >>Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level
2017 (English)In: 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017) / [ed] Gottumukkala, R Ning, X Dong, G Raghavan, V Aluru, S Karypis, G Miele, L Wu, X, IEEE , 2017, p. 170-176Conference paper, Published paper (Refereed)
Abstract [en]

In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large European telecommunication company, show that cluster validation techniques can be useful tools for assessing the organizational structure using objective analysis of internal email communications, and for simulating and studying different reorganization scenarios.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
Keywords
cluster validation measures, data analysis, human capital management, internal communication, organizational structure
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15992 (URN)10.1109/ICDMW.2017.28 (DOI)000425845700022 ()978-1-5386-3800-2 (ISBN)
Conference
17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-03-23Bibliographically approved
Shao, B., Lavesson, N., Boeva, V. & Shahzad, R. K. (2016). A mixture-of-experts approach for gene regulatory network inference. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 14(3), 258-275
Open this publication in new window or tab >>A mixture-of-experts approach for gene regulatory network inference
2016 (English)In: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, ISSN 1748-5673, Vol. 14, no 3, p. 258-275Article in journal (Refereed) Published
Abstract [en]

Gene regulatory network (GRN) inference is an important problem in bioinformatics. Many machine learning methods have been applied to increase the inference accuracy. Ensemble learning methods are shown in DREAM3 and DREAM5 challenges to yield a higher inference accuracy than individual algorithms. However, no ensemble method has been proposed to take advantage of the complementarity among existing algorithms from the perspective of network motifs. We propose an ensemble method based on the principle of Mixture-of-Experts ensemble learning. The method can quantitatively evaluate the accuracy of individual algorithms on predicting each type of the network motifs and assign weights to the algorithms accordingly. The individual predictions are then used to generate the ensemble prediction. By performing controlled experiments and statistical tests, the proposed ensemble method is shown to yield a significantly higher accuracy than the generic average ranking method used in the DREAM5 challenge. In addition, a new type of network motif is found in GRN, the inclusion of which can increase the accuracy of the proposed method significantly.

Place, publisher, year, edition, pages
InderScience Publishers, 2016
Keywords
GRN inference; ensemble learning; mixture-of-experts; network motif analysis
National Category
Information Systems Other Computer and Information Science
Identifiers
urn:nbn:se:bth-11852 (URN)10.1504/IJDMB.2016.074876 (DOI)000373392900004 ()
Available from: 2016-05-02 Created: 2016-05-02 Last updated: 2018-01-10Bibliographically approved
Borg, A., Lavesson, N. & Boeva, V. (2013). Comparison of clustering approaches for gene expression data. In: Frontiers in Artificial Intelligence and Applications: . Paper presented at Scandinavian Conference on Artificial Intelligence SCAI 2013 (pp. 55-64). IOS Press, 257
Open this publication in new window or tab >>Comparison of clustering approaches for gene expression data
2013 (English)In: Frontiers in Artificial Intelligence and Applications, IOS Press , 2013, Vol. 257, p. 55-64Conference paper, Published paper (Refereed)
Abstract [en]

Clustering algorithms have been used to divide genes into groups according to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently indicates that the genes could possibly share a common biological role. In this paper, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expression data using Dynamic TimeWarping distance in order to measure similarity between gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for estimating the quality of clusters, Jaccard Index for evaluating the stability of a cluster method and Rand Index for assessing the accuracy. The obtained results are analyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices.

Place, publisher, year, edition, pages
IOS Press, 2013
Keywords
Dynamic time warping, Gene expression data, Graph-based clustering algorithm, Minimum cut clustering, Partitioning algorithm
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-6357 (URN)10.3233/978-1-61499-330-8-55 (DOI)000343477100007 ()oai:bth.se:forskinfoF6F6EAEE6B2430D2C1257CA600374AA9 (Local ID)oai:bth.se:forskinfoF6F6EAEE6B2430D2C1257CA600374AA9 (Archive number)oai:bth.se:forskinfoF6F6EAEE6B2430D2C1257CA600374AA9 (OAI)
Conference
Scandinavian Conference on Artificial Intelligence SCAI 2013
Available from: 2015-05-25 Created: 2014-03-25 Last updated: 2018-01-11Bibliographically approved
García Martín, E., Lavesson, N., Grahn, H., Casalicchio, E. & Boeva, V. Hoeffding Trees with nmin adaptation.
Open this publication in new window or tab >>Hoeffding Trees with nmin adaptation
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution, which lead to energy hotspots. We present dynamic parameter adaptation for data stream mining algorithms to trade-off energy efficiency against accuracy during runtime. To validate this approach, we introduce the nmin adaptation method to improve parameter adaptation in Hoeffding trees. This method dynamically adapts the number of instances needed to make a split (nmin) and thereby reduces the overall energy consumption. We created an experiment to compare the Very Fast Decision Tree algorithm (VFDT, original Hoeffding tree algorithm) with nmin adaptation and the standard VFDT. The results show that VFDT with nmin adaptation consumes up to 89% less energy than the standard VFDT, trading off a few percent of accuracy. Our approach can be used to trade off energy consumption with predictive and computational performance in the strive towards resource-aware machine learning. 

Keywords
Hoeffding trees, data stream mining, green computing, green machine learning, energy efficiency
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15493 (URN)
Funder
Knowledge Foundation, 20140032
Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2018-02-02Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3128-191x

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