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Löfström, Tuve
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Publications (10 of 42) Show all publications
Löfström, T., Johansson, U., Balkow, J. & Sundell, H. (2018). A data-driven approach to online fitting services. In: Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium) (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: . Paper presented at 13th International FLINS Conference, Belfast, August 21-24, 2018. (pp. 1559-1566).
Open this publication in new window or tab >>A data-driven approach to online fitting services
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu (Ulster University, UK), Jie Lu (University of Technology Sydney, Australia), Yang Xu (Southwest Jiaotong University, China), Luis Martinez (University of Jaén, Spain) and Etienne E Kerre (University of Ghent, Belgium), 2018, p. 1559-1566Conference paper, Published paper (Refereed)
Abstract [en]

Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5% of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8%.

Keywords
Predictive regression, online fitting, fashion
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15824 (URN)10.1142/11069 (DOI)
Conference
13th International FLINS Conference, Belfast, August 21-24, 2018.
Projects
Datadriven innovation
Funder
Knowledge Foundation
Available from: 2019-02-25 Created: 2019-02-25 Last updated: 2019-02-25Bibliographically approved
Sundell, H., Löfström, T. & Johansson, U. (2018). Explorative multi-objective optimization of marketing campaigns for the fashion retail industry. In: Jun Liu, Jie Lu, Yang Xu, Luis Martinez and Etienne E Kerre (Ed.), Data Science and Knowledge Engineering for Sensing Decision Support: . Paper presented at FLINS 2018, Belfast, August 21-24, 2018. (pp. 1551-1558).
Open this publication in new window or tab >>Explorative multi-objective optimization of marketing campaigns for the fashion retail industry
2018 (English)In: Data Science and Knowledge Engineering for Sensing Decision Support / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez and Etienne E Kerre, 2018, p. 1551-1558Conference paper, Published paper (Refereed)
Abstract [en]

We show how an exploratory tool for association rule mining can be used for efficient multi-objective optimization of marketing campaigns for companies within the fashion retail industry. We have earlier designed and implemented a novel digital tool for mining of association rules from given basket data. The tool supports efficient finding of frequent itemsets over multiple hierarchies and interactive visualization of corresponding association rules together with numerical attributes. Normally when optimizing a marketing campaign, factors that cause an increased level of activation among the recipients could in fact reduce the profit, i.e., these factors need to be balanced, rather than optimized individually. Using the tool we can identify important factors that influence the search for an optimal campaign in respect to both activation and profit. We show empirical results from a real-world case-study using campaign data from a well-established company within the fashion retail industry, demonstrating how activation and profit can be simultaneously targeted, using computer-generated algorithms as well as human-controlled visualization.

Keywords
Association rules, marketing, visualization, Pareto front
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15138 (URN)
Conference
FLINS 2018, Belfast, August 21-24, 2018.
Funder
Knowledge Foundation, 20160035
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-02Bibliographically approved
Johansson, U., Löfström, T., Sundell, H., Linnusson, H., Gidenstam, A. & Boström, H. (2018). Venn predictors for well-calibrated probability estimation trees. In: Alex J. Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni N. Smirnov and Ralf L. M. Peeter (Ed.), 7th Symposium on Conformal and Probabilistic Prediction and Applications: COPA 2018, 11-13 June 2018, Maastricht, The Netherlands. Paper presented at 7th Symposium on Conformal and Probabilistic Prediction and Applications, London, June 11th - 13th, 2018 (pp. 3-14).
Open this publication in new window or tab >>Venn predictors for well-calibrated probability estimation trees
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2018 (English)In: 7th Symposium on Conformal and Probabilistic Prediction and Applications: COPA 2018, 11-13 June 2018, Maastricht, The Netherlands / [ed] Alex J. Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni N. Smirnov and Ralf L. M. Peeter, 2018, p. 3-14Conference paper, Published paper (Refereed)
Abstract [en]

Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. The standard solution is to employ an additional step, transforming the outputs from a classifier into probability estimates. In this paper, Venn predictors are compared to Platt scaling and isotonic regression, for the purpose of producing well-calibrated probabilistic predictions from decision trees. The empirical investigation, using 22 publicly available datasets, showed that the probability estimates from the Venn predictor were extremely well-calibrated. In fact, in a direct comparison using the accepted reliability metric, the Venn predictor estimates were the most exact on every data set.

Series
Proceedings of Machine Learning Research
Keywords
Venn predictors, Calibration, Decision trees, Reliability
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
urn:nbn:se:hb:diva-15061 (URN)
Conference
7th Symposium on Conformal and Probabilistic Prediction and Applications, London, June 11th - 13th, 2018
Funder
Knowledge Foundation
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2018-09-06Bibliographically approved
Linusson, H., Norinder, U., Boström, H., Johansson, U. & Löfström, T. (2017). On the Calibration of Aggregated Conformal Predictors. In: Proceedings of Machine Learning Research: . Paper presented at Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017.
Open this publication in new window or tab >>On the Calibration of Aggregated Conformal Predictors
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2017 (English)In: Proceedings of Machine Learning Research, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Conformal prediction is a learning framework that produces models that associate witheach of their predictions a measure of statistically valid confidence. These models are typi-cally constructed on top of traditional machine learning algorithms. An important result ofconformal prediction theory is that the models produced are provably valid under relativelyweak assumptions—in particular, their validity is independent of the specific underlyinglearning algorithm on which they are based. Since validity is automatic, much research onconformal predictors has been focused on improving their informational and computationalefficiency. As part of the efforts in constructing efficient conformal predictors, aggregatedconformal predictors were developed, drawing inspiration from the field of classification andregression ensembles. Unlike early definitions of conformal prediction procedures, the va-lidity of aggregated conformal predictors is not fully understood—while it has been shownthat they might attain empirical exact validity under certain circumstances, their theo-retical validity is conditional on additional assumptions that require further clarification.In this paper, we show why validity is not automatic for aggregated conformal predictors,and provide a revised definition of aggregated conformal predictors that gains approximatevalidity conditional on properties of the underlying learning algorithm.

National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-13636 (URN)
Conference
Conformal and Probabilistic Prediction and Applications, Stockholm Sweden 13-16 June, 2017
Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-03-14Bibliographically approved
Linusson, H., Johansson, U., Boström, H. & Löfström, T. (2016). Reliable Confidence Predictions Using Conformal Prediction. In: Lecture Notes in Computer Science: . Paper presented at PAKDD 2016: Advances in Knowledge Discovery and Data Mining, Auckland, April 19-22, 2016 (pp. 77-88). , 9651
Open this publication in new window or tab >>Reliable Confidence Predictions Using Conformal Prediction
2016 (Swedish)In: Lecture Notes in Computer Science, 2016, Vol. 9651, p. 77-88Conference paper, Published paper (Refereed)
Abstract [en]

Conformal classiers output condence prediction regions, i.e., multi-valued predictions that are guaranteed to contain the true output value of each test pattern with some predened probability. In order to fully utilize the predictions provided by a conformal classier, it is essential that those predictions are reliable, i.e., that a user is able to assess the quality of the predictions made. Although conformal classiers are statistically valid by default, the error probability of the prediction regions output are dependent on their size in such a way that smaller, and thus potentially more interesting, predictions are more likely to be incorrect. This paper proposes, and evaluates, a method for producing rened error probability estimates of prediction regions, that takes their size into account. The end result is a binary conformal condence predictor that is able to provide accurate error probability estimates for those prediction regions containing only a single class label.

National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-11963 (URN)
Conference
PAKDD 2016: Advances in Knowledge Discovery and Data Mining, Auckland, April 19-22, 2016
Available from: 2017-03-01 Created: 2017-03-01 Last updated: 2018-01-13Bibliographically approved
Radon, A., Johansson, P., Sundström, M., Alm, H., Behre, M., Göbel, H., . . . Wallström, S. (2016). What happens when retail meets research?: Special session. In: : . Paper presented at ANZMAC Conference 2016 - Marketing in a Post-Disciplinary Era, Christchurch, 5-7 December, 2016.
Open this publication in new window or tab >>What happens when retail meets research?: Special session
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2016 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

special session Information

We are witnessing the beginning of a seismic shift in retail due to digitalization. However, what is meant by digitalization is less clear. Sometimes it is understood as means for automatization and sometimes it is regarded as equal to e-commerce. Sometimes digitalization is considered being both automatization and e-commerce trough new technology. In recent years there has been an increase in Internet and mobile devise usage within the retail sector and e-commerce is growing, encompassing both large and small retailers. Digital tools such as, new applications are developing rapidly in order to search for information about products based on price, health, environmental and ethical considerations, and also to facilitate payments. Also the fixed store settings are changing due to digitalization and at an overall level; digitalization will lead to existing business models being reviewed, challenged and ultimately changed. More specifically, digitalization has consequences for all parts of the physical stores including customer interface, knowledge creation, sustainability performance and logistics. As with all major shifts, digitalization comprises both opportunities and challenges for retail firms and employees, and these needs to be empirically studied and systematically analysed. The Swedish Institute for Innovative Retailing at University of Borås is a research centre with the aim of identifying and analysing emerging trends that digitalization brings for the retail industry.

National Category
Economics and Business
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-11892 (URN)
Conference
ANZMAC Conference 2016 - Marketing in a Post-Disciplinary Era, Christchurch, 5-7 December, 2016
Available from: 2017-02-08 Created: 2017-02-08 Last updated: 2017-05-02Bibliographically approved
Löfström, T., Boström, H., Linusson, H. & Johansson, U. (2015). Bias Reduction through Conditional Conformal Prediction. Intelligent Data Analysis, 19(6), 1355-1375
Open this publication in new window or tab >>Bias Reduction through Conditional Conformal Prediction
2015 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 19, no 6, p. 1355-1375Article in journal (Refereed) In press
National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-756 (URN)10.3233/IDA-150786 (DOI)
Projects
High-Performance Data Mining for Drug Effect Detection (DADEL)
Available from: 2015-09-11 Created: 2015-09-11 Last updated: 2018-06-14Bibliographically approved
Löfström, T. (2015). On Effectively Creating Ensembles of Classifiers: Studies on Creation Strategies, Diversity and Predicting with Confidence. (Doctoral dissertation). Stockholm: Department of Computer and Systems Sciences, Stockholm University
Open this publication in new window or tab >>On Effectively Creating Ensembles of Classifiers: Studies on Creation Strategies, Diversity and Predicting with Confidence
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

An ensemble is a composite model, combining the predictions from several other models. Ensembles are known to be more accurate than single models. Diversity has been identified as an important factor in explaining the success of ensembles. In the context of classification, diversity has not been well defined, and several heuristic diversity measures have been proposed. The focus of this thesis is on how to create effective ensembles in the context of classification. Even though several effective ensemble algorithms have been proposed, there are still several open questions regarding the role diversity plays when creating an effective ensemble. Open questions relating to creating effective ensembles that are addressed include: what to optimize when trying to find an ensemble using a subset of models used by the original ensemble that is more effective than the original ensemble; how effective is it to search for such a sub-ensemble; how should the neural networks used in an ensemble be trained for the ensemble to be effective? The contributions of the thesis include several studies evaluating different ways to optimize which sub-ensemble would be most effective, including a novel approach using combinations of performance and diversity measures. The contributions of the initial studies presented in the thesis eventually resulted in an investigation of the underlying assumption motivating the search for more effective sub-ensembles. The evaluation concluded that even if several more effective sub-ensembles exist, it may not be possible to identify which sub-ensembles would be the most effective using any of the evaluated optimization measures. An investigation of the most effective ways to train neural networks to be used in ensembles was also performed. The conclusions are that effective ensembles can be obtained by training neural networks in a number of different ways but that high average individual accuracy or much diversity both would generate effective ensembles. Several findings regarding diversity and effective ensembles presented in the literature in recent years are also discussed and related to the results of the included studies. When creating confidence based predictors using conformal prediction, there are several open questions regarding how data should be utilized effectively when using ensembles. Open questions related to predicting with confidence that are addressed include: how can data be utilized effectively to achieve more efficient confidence based predictions using ensembles; how do problems with class imbalance affect the confidence based predictions when using conformal prediction? Contributions include two studies where it is shown in the first that the use of out-of-bag estimates when using bagging ensembles results in more effective conformal predictors and it is shown in the second that a conformal predictor conditioned on the class labels to avoid a strong bias towards the majority class is more effective on problems with class imbalance. The research method used is mainly inspired by the design science paradigm, which is manifested by the development and evaluation of artifacts. 

Abstract [sv]

En ensemble är en sammansatt modell som kombinerar prediktionerna från flera olika modeller. Det är välkänt att ensembler är mer träffsäkra än enskilda modeller. Diversitet har identifierats som en viktig faktor för att förklara varför ensembler är så framgångsrika. Diversitet hade fram tills nyligen inte definierats entydigt för klassificering vilket resulterade i att många heuristiska diverstitetsmått har föreslagits. Den här avhandlingen fokuserar på hur klassificeringsensembler kan skapas på ett ändamålsenligt (eng. effective) sätt. Den vetenskapliga metoden är huvudsakligen inspirerad av design science-paradigmet vilket lämpar sig väl för utveckling och evaluering av IT-artefakter. Det finns sedan tidigare många framgångsrika ensembleralgoritmer men trots det så finns det fortfarande vissa frågetecken kring vilken roll diversitet spelar vid skapande av välpresterande (eng. effective) ensemblemodeller. Några av de frågor som berör diversitet som behandlas i avhandlingen inkluderar: Vad skall optimeras när man söker efter en delmängd av de tillgängliga modellerna för att försöka skapa en ensemble som är bättre än ensemblen bestående av samtliga modeller; Hur väl fungerar strategin att söka efter sådana delensembler; Hur skall neurala nätverk tränas för att fungera så bra som möjligt i en ensemble? Bidraget i avhandlingen inkluderar flera studier som utvärderar flera olika sätt att finna delensembler som är bättre än att använda hela ensemblen, inklusive ett nytt tillvägagångssätt som utnyttjar en kombination av både diversitets- och prestandamått. Resultaten i de första studierna ledde fram till att det underliggande antagandet som motiverar att söka efter delensembler undersöktes. Slutsatsen blev, trots att det fanns flera delensembler som var bättre än hela ensemblen, att det inte fanns något sätt att identifiera med tillgänglig data vilka de bättre delensemblerna var. Vidare undersöktes hur neurala nätverk bör tränas för att tillsammans samverka så väl som möjligt när de används i en ensemble. Slutsatserna från den undersökningen är att det är möjligt att skapa välpresterande ensembler både genom att ha många modeller som är antingen bra i genomsnitt eller olika varandra (dvs diversa). Insikter som har presenterats i litteraturen under de senaste åren diskuteras och relateras till resultaten i de inkluderade studierna. När man skapar konfidensbaserade modeller med hjälp av ett ramverk som kallas för conformal prediction så finns det flera frågor kring hur data bör utnyttjas på bästa sätt när man använder ensembler som behöver belysas. De frågor som relaterar till konfidensbaserad predicering inkluderar: Hur kan data utnyttjas på bästa sätt för att åstadkomma mer effektiva konfidensbaserade prediktioner med ensembler; Hur påverkar obalanserad datade konfidensbaserade prediktionerna när man använder conformal perdiction? Bidragen inkluderar två studier där resultaten i den första visar att det mest effektiva sättet att använda data när man har en baggingensemble är att använda sk out-of-bag estimeringar. Resultaten i den andra studien visar att obalanserad data behöver hanteras med hjälp av en klassvillkorad konfidensbaserad modell för att undvika en stark tendens att favorisera majoritetsklassen.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. p. 82
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-009
Keywords
Machine Learning, Predictive Modeling, Ensembles, Conformal Prediction
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:hb:diva-121 (URN)978-91-7649-179-9 (ISBN)
Public defence
2015-06-11, L30, NOD-huset, Borgarfjordsgatan 12, Kista, 10:00 (English)
Opponent
Supervisors
Projects
Dataanalys för detektion av läkemedelseffekter (DADEL)
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: In press.

Available from: 2015-05-29 Created: 2015-05-26 Last updated: 2018-01-11Bibliographically approved
Löfström, T., Zhao, J., Linnusson, H. & Jansson, K. (2015). Predicting Adverse Drug Events with Confidence. In: Sławomir Nowaczyk (Ed.), Thirteenth Scandinavian Conference on Artificial Intelligence: . Paper presented at Thirteenth Scandinavian Conference on Artificial Intelligence. IOS Press
Open this publication in new window or tab >>Predicting Adverse Drug Events with Confidence
2015 (English)In: Thirteenth Scandinavian Conference on Artificial Intelligence / [ed] Sławomir Nowaczyk, IOS Press, 2015Conference paper, Published paper (Refereed)
Abstract [en]

This study introduces the conformal prediction framework to the task of predicting the presence of adverse drug events in electronic health records with an associated measure of statistically valid confidence. The imbalanced nature of the problem was addressed both by evaluating different machine learning algorithms, and by comparing different types of conformal predictors. A novel solution was also evaluated, where different underlying models, each model optimized towards one particular class, were combined into a single conformal predictor. This novel solution proved to be superior to previously existing approaches.

Place, publisher, year, edition, pages
IOS Press, 2015
Series
Frontiers in Artificial Intelligence and Applications
Keywords
Adverse Drug Events, Class Imbalance, Conformal Prediction, Predicting with Confidence.
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-3807 (URN)10.3233/978-1-61499-589-0-88 (DOI)978-1-61499-589-0 (ISBN)
Conference
Thirteenth Scandinavian Conference on Artificial Intelligence
Projects
High-Performance, Data Mining, Drug Effect Detection
Funder
Swedish Foundation for Strategic Research
Available from: 2015-12-08 Created: 2015-12-08 Last updated: 2018-01-10Bibliographically approved
Löfström, T., Linnusson, H., Sönströd, C. & Johansson, U. (2015). System Health Monitoring using Conformal Anomaly Detection. Högskolan i Borås
Open this publication in new window or tab >>System Health Monitoring using Conformal Anomaly Detection
2015 (English)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Högskolan i Borås, 2015. p. 20
Keywords
System health monitoring, conformal anomaly detection
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-9951 (URN)
Projects
Promoting and Enhancing Reuse of Information throughout the Content Lifecycle taking account of Evolving Semantics (PERICLES)
Funder
EU, FP7, Seventh Framework Programme
Note

Technical report

Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2018-01-10Bibliographically approved
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