Change search
Link to record
Permanent link

Direct link
BETA
Sundell, Håkan
Publications (10 of 24) 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
Show others...
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
Johansson, U., Sundström, M., Håkan, S., Rickard, K. & Jenny, B. (2016). Dataanalys för ökad kundförståelse. Stockholm: Handelsrådet
Open this publication in new window or tab >>Dataanalys för ökad kundförståelse
Show others...
2016 (Swedish)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
Stockholm: Handelsrådet, 2016. p. 66
National Category
Business Administration
Identifiers
urn:nbn:se:hb:diva-12080 (URN)
Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2017-05-02Bibliographically 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
Show others...
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
Tavara, S., Sundell, H. & Dahlbom, A. (2015). Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM. In: : . Paper presented at The 21st International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 15).
Open this publication in new window or tab >>Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM
2015 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-4062 (URN)
Conference
The 21st International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 15)
Available from: 2015-12-15 Created: 2015-12-15 Last updated: 2018-01-10Bibliographically approved
Sundell, H., König, R. & Johansson, U. (2015). Pragmatic Approach to Association Rule Learning in Real-World Scenarios. In: : . Paper presented at The 2015 International Conference on Computational Science and Computational Intelligence (CSCI'15).
Open this publication in new window or tab >>Pragmatic Approach to Association Rule Learning in Real-World Scenarios
2015 (English)Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-8515 (URN)
Conference
The 2015 International Conference on Computational Science and Computational Intelligence (CSCI'15)
Available from: 2016-01-14 Created: 2016-01-14 Last updated: 2018-01-10Bibliographically approved
Jansson, K., Sundell, H. & Boström, H. (2014). gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles. In: : . Paper presented at IEEE International Parallel & Distributed Processing Symposium 2014, Phoenix, USA. IEEE Computer Society
Open this publication in new window or tab >>gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present two new parallel implementations of the ensemble learning methods Random Forests (RF) and Extremely Randomized Trees (ERT), called gpuRF and gpuERT, for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). RF and ERT are two ensemble methods for generating predictive models that are of high importance within machine learning. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for RF in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler architecture and relatively few cores. For ERT, only briefly sketched parallelization attempts exist in the literature. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures, such as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface to attain the best possible performance on NVidia-based GPUs. An experimental study comparing the most important previous solutions for CPU and GPU platforms to the novel implementations shows significant advantages in the aspect of efficiency for the latter, often with several orders of magnitude.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Keywords
GPGPU, Decision Tree Ensambles, Parallel Processing, Machine Learning, Random Forest, Extremely Randomized Trees, Computer Science
National Category
Computer Sciences Software Engineering
Identifiers
urn:nbn:se:hb:diva-7253 (URN)2320/14447 (Local ID)978-1-4799-4116-2 (ISBN)2320/14447 (Archive number)2320/14447 (OAI)
Conference
IEEE International Parallel & Distributed Processing Symposium 2014, Phoenix, USA
Funder
Swedish Foundation for Strategic Research
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10Bibliographically approved
Jansson, K., Sundell, H. & Boström, H. (2014). gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles. In: Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International: . Paper presented at Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International, May 19-23, 2014. (pp. 1612-1621).
Open this publication in new window or tab >>gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles
2014 (English)In: Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International, 2014, p. 1612-1621Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:hb:diva-4063 (URN)
Conference
Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International, May 19-23, 2014.
Available from: 2015-12-15 Created: 2015-12-15 Last updated: 2018-06-14Bibliographically approved
Nguyen, N., Tsigas, P. & Sundell, H. (2014). ParMarkSplit: A Parallel Mark-Split Garbage Collector Based on a Lock-Free Skip-List. In: Marcos K. Aguilera, Marc Shapiro (Ed.), : . Paper presented at OPODIS 2014. The 18th International Conference on Principles of Distributed Systems, Cortina d'Ampezzo, Italy, 16-19 December, 2014. Springer
Open this publication in new window or tab >>ParMarkSplit: A Parallel Mark-Split Garbage Collector Based on a Lock-Free Skip-List
2014 (English)In: / [ed] Marcos K. Aguilera, Marc Shapiro, Springer , 2014Conference paper, Published paper (Refereed)
Abstract [en]

Mark-split is a garbage collection algorithm that combines advantages of both the mark-sweep and the copying collection algorithms. In this paper, we present a parallel mark-split garbage collector (GC). Our parallel design introduces and makes use of an efficient concurrency control mechanism for handling the list of free memory intervals. This mechanism is based on a lock-free skip-list design which supports an extended set of operations. Beside basic operations, it can perform a composite one that can search and remove and also insert two elements atomically. We have implemented the parallel mark-split GC in OpenJDK’s HotSpot virtual machine. We experimentally evaluate our collector and compare it with the default concurrent mark-sweep GC in HotSpot, using the DaCapo benchmarks, on two contemporary multiprocessor systems; one has 12 Intel Nehalem cores with HyperThreading and the other has 48 AMD Bulldozer cores. The evaluation shows that our parallel mark-split keeps the characteristics of the sequential mark-split, that it performs better than the concurrent mark-sweep in applications that have low live/garbage ratio, and have live objects locating contiguously, therefore being marked consecutively. Our parallel mark-split performs significantly better than a trivial parallelization based on locks in terms of both collection time and scalability.

Place, publisher, year, edition, pages
Springer, 2014
Series
Lecture Notes in Computer Science ; 8878
Keywords
garbage collector, concurrent programming, mark-split, mark-sweep, parallel garbage collection, lock-free data structures, Parallel Computing
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-7256 (URN)2320/14719 (Local ID)2320/14719 (Archive number)2320/14719 (OAI)
Conference
OPODIS 2014. The 18th International Conference on Principles of Distributed Systems, Cortina d'Ampezzo, Italy, 16-19 December, 2014
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10Bibliographically approved
Organisations

Search in DiVA

Show all publications