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Boström, Henrik
Publications (7 of 7) Show all publications
Vasiloudis, T., Cho, H. & Boström, H. (2019). Block-distributed Gradient Boosted Trees. In: : . Paper presented at 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Open this publication in new window or tab >>Block-distributed Gradient Boosted Trees
2019 (English)Conference paper, Published paper (Refereed)
Keywords
Gradient Boosted Trees, Distributed Systems, Communication Efficiency, Scalability
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-249994 (URN)
Conference
42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Funder
Swedish Foundation for Strategic Research , BD15-0006
Note

QC 20190426

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-05-21Bibliographically approved
Gammerman, A., Vovk, V., Boström, H. & Carlsson, L. (2019). Conformal and probabilistic prediction with applications: editorial. Machine Learning, 108(3), 379-380
Open this publication in new window or tab >>Conformal and probabilistic prediction with applications: editorial
2019 (English)In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, no 3, p. 379-380Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
SPRINGER, 2019
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-246240 (URN)10.1007/s10994-018-5761-x (DOI)000459945900001 ()2-s2.0-85052674006 (Scopus ID)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-06-11Bibliographically approved
Johansson, U., Lofstrom, T., Linusson, H. & Boström, H. (2019). Efficient Venn predictors using random forests. Machine Learning, 108(3), 535-550
Open this publication in new window or tab >>Efficient Venn predictors using random forests
2019 (English)In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, no 3, p. 535-550Article in journal (Refereed) Published
Abstract [en]

Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. In addition, a probabilistic classifier must, of course, also be as accurate as possible. In this paper, Venn predictors, and its special case Venn-Abers predictors, are evaluated for probabilistic classification, using random forests as the underlying models. Venn predictors output multiple probabilities for each label, i.e., the predicted label is associated with a probability interval. Since all Venn predictors are valid in the long run, the size of the probability intervals is very important, with tighter intervals being more informative. The standard solution when calibrating a classifier is to employ an additional step, transforming the outputs from a classifier into probability estimates, using a labeled data set not employed for training of the models. For random forests, and other bagged ensembles, it is, however, possible to use the out-of-bag instances for calibration, making all training data available for both model learning and calibration. This procedure has previously been successfully applied to conformal prediction, but was here evaluated for the first time for Venn predictors. The empirical investigation, using 22 publicly available data sets, showed that all four versions of the Venn predictors were better calibrated than both the raw estimates from the random forest, and the standard techniques Platt scaling and isotonic regression. Regarding both informativeness and accuracy, the standard Venn predictor calibrated on out-of-bag instances was the best setup evaluated. Most importantly, calibrating on out-of-bag instances, instead of using a separate calibration set, resulted in tighter intervals and more accurate models on every data set, for both the Venn predictors and the Venn-Abers predictors.

Place, publisher, year, edition, pages
SPRINGER, 2019
Keywords
Probabilistic prediction, Venn predictors, Venn-Abers predictors, Random forests, Out-of-bag calibration
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-246235 (URN)10.1007/s10994-018-5753-x (DOI)000459945900008 ()2-s2.0-85052523706 (Scopus ID)
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved
Linusson, H., Johansson, U., Boström, H. & Löfström, T. (2018). Classification with Reject Option Using Conformal Prediction. In: Phung, D Tseng, VS Webb, GI Ho, B Ganji, M Rashidi, L (Ed.), Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I: . Paper presented at 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), JUN 03-06, 2018, Deakin Univ, Melbourne, Australia (pp. 94-105). Springer, 10937
Open this publication in new window or tab >>Classification with Reject Option Using Conformal Prediction
2018 (English)In: Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I / [ed] Phung, D Tseng, VS Webb, GI Ho, B Ganji, M Rashidi, L, Springer, 2018, Vol. 10937, p. 94-105Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a practically useful means of interpreting the predictions produced by a conformal classifier. The proposed interpretation leads to a classifier with a reject option, that allows the user to limit the number of erroneous predictions made on the test set, without any need to reveal the true labels of the test objects. The method described in this paper works by estimating the cumulative error count on a set of predictions provided by a conformal classifier, ordered by their confidence. Given a test set and a user-specified parameter k, the proposed classification procedure outputs the largest possible amount of predictions containing on average at most k errors, while refusing to make predictions for test objects where it is too uncertain. We conduct an empirical evaluation using benchmark datasets, and show that we are able to provide accurate estimates for the error rate on the test set.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743 ; 10937
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-235161 (URN)10.1007/978-3-319-93034-3_8 (DOI)000443224400008 ()2-s2.0-85049360232 (Scopus ID)9783319930336 (ISBN)
Conference
22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), JUN 03-06, 2018, Deakin Univ, Melbourne, Australia
Note

QC 20180917

Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-09-17Bibliographically approved
Linusson, H., Norinder, U., Boström, H., Johansson, U. & Löfström, T. (2017). On the calibration of aggregated conformal predictors. In: Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos (Ed.), Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden. Paper presented at The 6th Symposium on Conformal and Probabilistic Prediction with Applications, (COPA 2017), 13-16 June, 2017, Stockholm, Sweden (pp. 154-173).
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: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, 2017, p. 154-173Conference paper, Published paper (Refereed)
Abstract [en]

Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical 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 approximate validity conditional on properties of the underlying learning algorithm.

Keywords
Confidence Predictions, Conformal Prediction, Classification, Ensembles
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-221578 (URN)
Conference
The 6th Symposium on Conformal and Probabilistic Prediction with Applications, (COPA 2017), 13-16 June, 2017, Stockholm, Sweden
Note

QC 20180327

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-03-27Bibliographically approved
Karunaratne, T. & Boström, H. (2007). Using background knowledge for graph based learning: a case study in chemoinformatics. In: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II: . Paper presented at International Multiconference of Engineers and Computer Scientists. Kowloon, PEOPLES R CHINA. MAR 21-23, 2007 (pp. 153-157). HONG KONG: INT ASSOC ENGINEERS-IAENG
Open this publication in new window or tab >>Using background knowledge for graph based learning: a case study in chemoinformatics
2007 (English)In: IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, HONG KONG: INT ASSOC ENGINEERS-IAENG , 2007, p. 153-157Conference paper, Published paper (Refereed)
Abstract [en]

Incorporating background knowledge in the learning process is proven beneficial for numerous applications of logic based learning methods. Yet the effect of background knowledge in graph based learning is not systematically explored. This paper describes and demonstrates the first step in this direction and elaborates on how additional relevant background knowledge could be used to improve the predictive performance of a graph learner. A case study in chemoinformatics is undertaken in this regard in which various types of background knowledge are encoded in graphs that are given as input to a graph learner. It is shown that the type of background knowledge encoded indeed has an effect on the predictive performance, and it is concluded that encoding appropriate background knowledge can be more important than the choice of the graph learning algorithm.

Place, publisher, year, edition, pages
HONG KONG: INT ASSOC ENGINEERS-IAENG, 2007
Series
Lecture Notes in Engineering and Computer Science, ISSN 2078-0958
Keywords
graph propositionalization, machine learning, structured data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-39392 (URN)000246800600028 ()2-s2.0-84888342422 (Scopus ID)978-988-98671-4-0 (ISBN)
Conference
International Multiconference of Engineers and Computer Scientists. Kowloon, PEOPLES R CHINA. MAR 21-23, 2007
Note

QC 20110913

Available from: 2011-09-13 Created: 2011-09-09 Last updated: 2018-01-16Bibliographically approved
Rao, W., Boström, H. & Xie, S. (2004). Rule induction for structural damage identification. In: Proc. Int. Conf. Mach. Learning Cybernetics: . Paper presented at Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 26-29 August 2004, Shanghai, China (pp. 2865-2869).
Open this publication in new window or tab >>Rule induction for structural damage identification
2004 (English)In: Proc. Int. Conf. Mach. Learning Cybernetics, 2004, p. 2865-2869Conference paper, Published paper (Refereed)
Abstract [en]

Structural damage identification is becoming a worldwide research subject. Some machine learning methods have been used to solve this problem, and most of them are neural network methods. In this paper, three different rule inductive methods named as Divide-and-Conquer (DAC), Bagging and Separate-and-Conquer (SAC) are investigated for predicting the damage position and extent of a concrete beam. Then radial basis function neural network (RBFNN) is used here for comparative purposes. The rule inductive methods/ especially Bagging are shown to obtain good prediction.

Series
Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; 5
Keywords
Bagging, Divide-and-Conquer, Rule induction, Separate-and-Conquer, Structural damage identification, Backpropagation, Data acquisition, Elastic moduli, Information retrieval, Learning systems, Mathematical models, Multilayer neural networks, Radial basis function networks, Identification (control systems)
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-156946 (URN)000225293600564 ()2-s2.0-6344229873 (Scopus ID)0780384032 (ISBN)
Conference
Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 26-29 August 2004, Shanghai, China
Note

QC 20141205

Available from: 2014-12-05 Created: 2014-12-04 Last updated: 2018-01-16Bibliographically approved
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