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Henrik Boström, Professor
Dept. of Computer and Systems Sciences

Stockholm University
Post box 7003, SE-164 07 Kista
Sweden
Email: henrik.bostrom@dsv.su.se
Phone: +46 8 16 16 16
Fax: +46 8 703 90 25

My research interests are primarily within the area of machine learning, i.e., artificial systems that learn from experience. Research in this area may be found under several different headings, including data science, data mining, knowledge discovery, big data analytics, predictive modeling and intelligent data analysis. My research focus is on ensemble learning and interpretable models, including decision trees and rules.

I am editor of the journal Machine Learning and at the editorial boards of Data Mining and Knowledge Discovery, Journal of Machine Learning Research and Intelligent Data Analysis.

I am project leader of High-performance data mining for drug effect detection, a 19 MSEK project funded by the Swedish Foundation for Strategic Research during 2012-2016. See press release (in Swedish). I am also at the management board of the project Integrated Dynamic Prognostic Maintenance Support (IRIS), lead by Scania AB, and supported by 11.6 MSEK from Swedish Governmental Agency for Innovation Systems (VINNOVA) during 2012-2017.

I organized a national workshop on data science on Dec. 4 and 5, 2014 – see the program here as well as an interview on this event in ComputerSweden here (in Swedish). An earlier interview on the event can be found here (there is also a Swedish version here) as well as a previous interview on data science here (in Swedish here).

I am or have recently been at the following program committees:

KDD 2016
ECML/PKDD 2016
IDA 2016 – program chair
KDD 2015
ECML/PKDD 2015 – area chair
IDA 2015
KDD 2014
ECML/PKDD 2014 – area chair
IDA 2014
DS 2013

IDA 2013
ECML/PKDD 2013 – area chair
AAAI 2013
DS 2012
IDA 2012
ECML/PKDD 2012
ECAI 2012
Fusion 2012
KDD 2011
ICML 2011
AAAI 2011
IDA 2011
DS 2011
KDD 2010
ICML 2010 – area chair for rule and decision tree learning
ECML/PKDD 2010
KDD 2009

ICML 2009
ECML/PKDD 2009
Fusion 2009
HAIS 2009
ICML 2008

ECML/PKDD ’08

Previous PhD students:

  • PhD Martin Eineborg, during 1998 – 2002. His thesis is entitled Inductive logic programming for part of speech tagging
  • PhD Tony Lindgren , during 2000 – 2006. His thesis is entitled Methods of solving conflicts among induced rules
  • PhD Thashmee Karunaratne, during 2004 – 2014 (co-supervised by Lars Asker). Her thesis is entitled Learning predictive models from graph data using pattern mining
  • PhD Catarina Dudas, during 2007 – 2014 (co-supervised by Amos Ng). Her thesis is entitled Learning from Multi-Objective Optimization of Production Systems – A method for analyzing solution sets from multi-objective optimization
  • PhD Tuve Löfström, during 2007 – 2015 (co-supervised by Ulf Johansson). His thesis is entitled On Effectively Creating Ensembles of Classifiers: Studies on Creation Strategies, Diversity and Predicting with Confidence
  • PhD Orlando Zacarias, during 2010 – 2015 (co-supervised by Mats Danielson). His thesis is entitled Mining Mozambique Health Data: The Case of Malaria: From Bayesian Incidence Risk to Incidence Case Predictions

Current PhD students:

  • PhLic Sampath Deegalla, since 2004 (co-supervised by Keerthi Walgama). His licentiate thesis in 2009 was entitled Towards Improving Performance of Nearest Neighbor Classification in High Dimensions
  • Cecilia Sönströd, since 2009 (co-supervised by Ulf Johansson). She is working on methods for concept description.
  • Isak Karlsson, since 2012 (co-supervised by Lars Asker). He is working on data mining for analyzing electronic patient records.
  • Karl Jansson, since 2012 (co-supervisor, main supervisor is Håkan Sundell). He is working on parallel data mining.
  • Jing Zhao, since 2012 (co-supervisor, main supervisor is Lars Asker). She is working on data mining for analyzing electronic patient records.
  • Henrik Linusson, since 2013 (co-supervisor, main supervisor is Ulf Johansson). He is working on conformal prediction.
  • Ram Gurung, since 2013 (co-supervised by Tony Lindgren). He is working on data mining for predicting remaining useful life of truck components.

Curriculum Vitae

Publications

  1. I. Karlsson, P. Papapetrou and H. Boström, 2016. Early Random Shapelet Forest. Proc. of the 19th International Conference on Discovery Science. Springer (to appear) [Carl H. Smith Award for best paper]
  2. I. Karlsson and H. Boström, 2016. Predicting Adverse Drug Events using Heterogeneous Event Sequences. Proc. of the IEEE International Conference on Healthcare Informatics (ICHI 2016) (to appear)
  3. H. Boström, H. Linusson, T. Löfström and U. Johansson. 2016. Evaluation of a Variance-Based Nonconformity Measure for Regression Forests. Proc. of the International Symposium on Conformal and Probabilistic Prediction with Applications, Springer, pp. 75-89.
  4. L. Asker, H. Boström, P. Papapetrou and H. Persson. 2016. Identifying Factors for the Effectiveness of Treatment of Heart Failure: A Registry Study. CBMS, pp. 205-206
  5. R. Gurung, T. Lindgren and H. Boström. 2016. Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins. FLAIRS, pp. 430-435
  6. H. Linusson, U. Johansson, H. Boström and T. Löfström. 2016. Reliable Confidence Predictions Using Conformal Prediction. PAKDD, pp. 77-88
  7. I. Karlsson, P. Papapetrou and H. Boström, 2016. Generalized random shapelet forests. Data Min. Knowl. Discov. 30(5): 1053-1085
  8. A. Henriksson, J. Zhao, H. Dalianis, and H. Boström. 2016. Ensembles of randomized trees using diverse distributed representations of clinical events. BMC Med Inform Decis Mak. 16 (2), pp. 85-95
  9. C. Dudas, A. H. C. Ng and H. Boström. 2015. Post-analysis of multi-objective optimization solutions using decision trees. Intell. Data Anal. 19(2): 259-278.
  10. J. Zhao, A. Henriksson, L. Asker, and H. Boström. 2015. Predictive modeling of structured electronic health records for adverse drug event detection. BMC Med Inform Decis Mak. 15 (Suppl 4), S1
  11. T. Löfström, H. Boström, H. Linusson and U. Johansson. 2015. Bias reduction through conditional conformal prediction. Intelligent Data Analysis, 19(6), pp.1355-1375.
  12. A. Henriksson, J. Zhao, H. Boström and H. Dalianis. 2015. Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection. BIBM 2015: 343-350.
  13. A. Henriksson, J. Zhao, H. Boström and H. Dalianis. 2015. Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection. DSAA 2015: 1-8.
  14. J. Zhao, A. Henriksson and H. Boström. 2015. Cascading adverse drug event detection in electronic health records. DSAA 2015: 1-8.
  15. J. Zhao, A. Henriksson, M. Kvist, L. Asker and H. Boström. 2015. Handling temporality of clinical events for drug safety surveillance. AMIA Annual Symposium Proceedings (Vol. 2015, p. 1371). American Medical Informatics Association. [Winner of distinguished paper award]
  16. A. Henelius, K. Puolamäki, I. Karlsson, J. Zhao, L. Asker, H. Boström and P. Papapetrou.
    2015. GoldenEye++: A Closer Look into the Black Box. SLDS 2015: 96-105.
  17. I. Karlsson, Panagiotis Papapetrou and H. Boström. 2015. Forests of Randomized Shapelet Trees. SLDS 2015: 126-136.
  18. L. Carlsson, Ernst Ahlberg, H. Boström, Ulf Johansson and H. Linusson. 2015.
    Modifications to p-Values of Conformal Predictors. SLDS 2015: 251-259.
  19. U. Johansson, E. Ahlberg, H. Boström, L. Carlsson, H. Linusson and C. Sönströd. 2015.
    Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors. SLDS 2015: 271-280.
  20. C. Dudas, A. Ng, L. Pehrsson and H. Boström. 2014. Integration of data mining and multi-objective optimization for decision support in production system development. International Journal of Computer-Integrated Manufacturing, Vol. 27, Iss. 9.
  21. A. Henelius, K. Puolamäki, H. Boström, L. Asker and P. Papapetrou. 2014. A peek into the black box: exploring classifiers by randomization. Data mining and knowledge discovery 28 (5-6), pp. 1503-1529.
  22. U. Johansson, H. Boström, T. Löfström and H. Linusson 2014. Regression conformal prediction with random forests, Machine Learning, Vol. 97:1-2, pp 155-176.
  23. U. Johansson, C. Sönströd, H. Linusson, and H. Boström. 2014. Regression trees for streaming data with local performance guarantees, IEEE International Conference on Big Data, pp. 461-470, Washington D.C.
  24. H. Linusson, U. Johansson, H. Boström and T. Löfström. 2014. Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers, AIAI – COPA workshop, IFIP Advances in Information and Communication Technology Volume 437, pp 261-270.
  25. U. Johansson, R. König, H. Linusson, T. Löfström and H. Boström. 2014. Rule Extraction with Guaranteed Fidelity, AIAI – COPA workshop, IFIP Advances in Information and Communication Technology Volume 437,  pp 281-290.
  26. J. Zhao, A. Henriksson, L. Asker, H. Boström: Detecting adverse drug events with multiple representations of clinical measurements. BIBM 2014: 536-543.
  27. I. Karlsson, H. Boström: Handling Sparsity with Random Forests When Predicting Adverse Drug Events from Electronic Health Records. ICHI 2014: 17-22.
  28. J. Zhao, A. Henriksson, H. Boström: Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes. ICHI 2014: 285-293.
  29. K. Jansson, H. Sundell and H. Boström. 2014. gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles. IPDPS Workshops 2014: 1612-1621.
  30. L. Asker, H. Boström, I. Karlsson, P. Papapetrou and J. Zhao. 2014. Mining candidates for adverse drug interactions in electronic patient records. Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA), ACM.
  31. T. Karunaratne, H. Boström and U. Norinder. 2013. Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling, Intelligent Data Analysis 17 (2), pp. 327-341.
  32. Norinder, U. and Boström H., Representing descriptors derived from multiple conformations as uncertain features for machine learning. Journal of Molecular Modeling, Vol. 19, No. 6, pp. 2679-2685, Springer, 2013.
  33. O. Zacarias and H. Boström. Predicting the Incidence of Malaria Cases in Mozambique Using Regression Trees and Forests, International Journal of Computer Science and Electronics Engineering 1(1), pp. 50-54, 2013.
  34. Johansson, U., Boström, H. and Löfström, T. (2013), Conformal Prediction Using Decision Trees, IEEE International Conference on Data Mining (ICDM), pp. 330-339, Dallas, TX.
  35. Johansson, U., Löfström, T. and Boström, H. (2013), Random Brains, The International Joint Conference on Neural Networks (IJCNN), Dallas, TX, IEEE.
  36. Johansson, U., König, R., Löfström, T. and Boström, H. (2013), Evolved Decision Trees as Conformal Predictors, IEEE Congress on Evolutionary Computation (CEC), pp. 1794-1801, Cancun, Mexico.
  37. Johansson, U., Löfström, T. and Boström, H. (2013), Overproduce-and-Select: The Grim Reality, Computational Intelligence and Ensemble Learning, IEEE Symposium Series on Computational Intelligence (SSCI), pp. 52-59, Singapore.
  38. Karlsson I., J. Zhao, L. Asker and H. Boström, Predicting Adverse Drug Events by Analyzing Electronic Patient Records. Proc. of the 14th Conference on Artificial Intelligence in Medicine (AIME), Lecture Notes in Computer Science, Vol. 7885, pp. 125-129, Springer Publishing Company, 2013.
  39. T. Löfström, U. Johansson and H. Boström, Effective Utilization of Data in Inductive Conformal Prediction using Ensembles of Neural Networks. pp. 1-8, in IEEE conference proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), 2013.
  40. Zhao, J., Karlsson, I., Asker, L. and Boström, H. Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records. In 19th Knowledge Discovery and Data Mining (KDD) Conference’s Workshop on Data Mining for Healthcare (DMH), August 11-14, 2013, Chicago, USA.
  41. A. Ng, C. Dudas, H. Boström and K. Deb. Interleaving innovization with evolutionary multi-objective optimization in production system simulation for faster convergence. In Proceedings of Learning and Intelligent Optimization Conference, LION 2013, Springer (winner of best paper award)
  42. O. Zacarias and H. Boström. Strengthening the Health Information System in Mozambique through Malaria Incidence Prediction. Proc. of IST Africa, International Information Management Corporation (IIMC), 29-31 May, 2013.
  43. O. Zacarias and H. Boström, Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias. International Conference on Advanced Data Mining and Applications, Springer-Verlag, pp. 189-200, 2013.
  44. O. Zacarias and H. Boström. Comparing Support Vectort Regression and Random Forests Modelling for Predicting Malaria Incidence in Mozambique. Proc. of International Conference on Advances in ICT for Emerging Regions, IEEE, pp. 217-221, 2013.
  45. C. Sotomane, L. Asker, H. Boström, V. Massingue. Short-term Forecasting of Electricity Consumption in Maputo. Proc. of International Conference on Advances in ICT for Emerging Regions, IEEE, pp. 132-136, 2013.
  46. C. Sotomane, L. Asker, H. Boström, V. Massingue. Factors Affecting the Use of Data Mining in Mozambique. Proc. of IST Africa, International Information Management Corporation (IIMC), 2013.
  47. H. Boström and H. Dalianis (2012). De-identifying health records by means of active learning. In Proc. of ICML Workshop on Machine Learning for Clinical Data Analysis.
  48. H. Dalianis and H. Boström. Releasing a Swedish Clinical Corpus after Removing all Words – De-identification Experiments with Conditional Random Fields and Random Forests. In Proc. of Third LREC Workshop on Building and Evaluating Resources for Biomedical Text Mining, 2012.
  49. T. Karunaratne and H. Boström. Can frequent itemset mining be efficiently and effectively used for learning from graph data?, In Proc. of 11th International Conference on Machine Learning and Applications, pp. 409-414, 2012.
  50. S. Deegalla, H. Boström and K. Walgama. Choice of Dimensionality Reduction Methods for Feature and Classifier Fusion with Nearest Neighbor Classifiers. Proc. of the 15th International Conference on Information Fusion, pages 875-881, 2012.
  51. H. Boström. Forests of probability estimation trees. International Journal of Pattern Recognition and Artificial Intelligence, 26 (2), 2012.
  52. U. Johansson, C. Sönströd, T. Löfström and H. Boström. Obtaining accurate and comprehensible classifiers using oracle coaching. Intelligent Data Analysis, 16(2), pages 247-263, 2012.
  53. H. Boström. Concurrent Learning of Large-Scale Random Forests. In Proceedings of Scandinavian Conference on Artificial Intelligence, pages 20-29, 2011. Available from: http://people.dsv.su.se/˜henke/papers/bostrom11.pdf
  54. U. Johansson, C. Sönströd, U. Norinder and H. Boström H. The Trade-Off between Accuracy and Interpretability for Predictive In Silico Modeling. Future Medicinal Chemistry, 3(6), pages 647-663, 2011.
  55. T. Karunaratne, H. Boström, and U. Norinder. Pre-processing structured data for standard machine learning algorithms by supervised graph propositionalization – a case study with medicinal chemistry datasets. In Proceedings of the Ninth International Conference on Machine Learning and Applications. IEEE Computer Society, 2010.
    Available from: http://people.dsv.su.se/˜henke/papers/karunaratne10.pdf
  56. U. Johansson, C. Sönströd, U. Norinder, H. Boström, and T. Löfström. Using feature selection with bagging and rule extraction in drug discovery. In Advances in Intelligent Decision Technologies, Second KES International Symposium IDT 2010, pages 413–422, 2010.
    Available from: http://people.dsv.su.se/˜henke/papers/johansson10.pdf
  57. C. Sönströd, U. Johansson, H. Boström, and U. Norinder. Pin-pointing concept descriptions. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pages 2956–2963, 2010.
    Available from: http://people.dsv.su.se/˜henke/papers/sonstrod10.pdf
  58. T. Löfström, U. Johansson, and H. Boström. Implicit vs. explicit methods for generating diverse ensembles of artificial neural networks. In WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010, pages 1287–1292, 2010.
    Available from: http://people.dsv.su.se/˜henke/papers/lofstrom10.pdf
  59. T. Karunaratne and H. Boström. Graph propositionalization for random forests. In Proceedings of the Eighth International Conference on Machine Learning and Applications, pages 196–201. IEEE Computer Society, 2009.
    Available from: http://people.dsv.su.se/˜henke/papers/karunaratne09.pdf
  60. S. Deegalla and H. Boström. Improving fusion of dimensionality reduction methods for nearest neighbor classification. In Proceedings of the Eighth International Conference on Machine Learning and Applications, pages 771–775. IEEE Computer Society, 2009.
    Available from: http://people.dsv.su.se/˜henke/papers/deegalla09b.pdf
  61. S. Deegalla and H. Boström. Fusion of dimensionality reduction methods: A case study in microarray classification. In Proceedings of the 12th International Conference on Information Fusion, pages 460–465, 2009.
    Available from: http://people.dsv.su.se/˜henke/papers/deegalla09a.pdf
  62. H. Boström and U. Norinder. Utilizing Information on Uncertainty for In Silico Modeling using Random Forests, Proc. of the 3rd Skövde Workshop on Information Fusion Topics, pp 59-62, 2009.
  63. C. Dudas and H. Boström. Using uncertain chemical and thermal data to predict product quality in a casting process. In Proceedings of the First ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, pages 57–61, 2009. Available from: http://people.dsv.su.se/˜henke/papers/dudas09b.pdf
  64. C. Dudas, A. Ng, and H. Boström. Information extraction from solution set of multi-objective simulation optimisation using data mining. In Proceedings of Industrial Simulation Conference, pages 65–69, 2009.
    Available from: http: //people.dsv.su.se/˜henke/papers/dudas09a.pdf
  65. T. Löfström, U. Johansson, and H. Boström. Ensemble member selection using multi-objective optimization. In Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, pages 245–251, 2009.
    Available from: http://people.dsv.su.se/˜henke/papers/lofstrom09.pdf
  66. H. Boström. Calibrating random forests. In Proceedings of the Seventh International Conference on Machine Learning and Applications, pages 121–126. IEEE Computer Society, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom08b.pdf
  67. T. Löfström, U. Johansson, and H. Boström. On the use of accuracy and diversity measures for evaluating and selecting ensembles of classifiers. In Proceedings of the Seventh International Conference on Machine Learning and Applications, pages 127–132. IEEE Computer Society, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/lofstrom08.pdf
  68. C. Sönströd, U. Johansson, U. Norinder, and H. Boström. Comprehensible models for predicting molecular interaction with heart-regulating genes. In Proceedings of Seventh International Conference on Machine Learning and Applications, pages 559–564. IEEE Computer Society, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/sonstrod08.pdf
  69. C. Dudas, A. Ng, and H. Boström. Information extraction in manufacturing using data mining techniques. In Proceedings of Swedish Production Symposium, pages 111–118, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/dudas08.pdf
  70. R. Johansson, H. Boström, and A. Karlsson. A study on class-specifically discounted belief for ensemble classifiers. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pages 614–619, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/johansson08d.pdf
  71. H. Boström, R. Johansson, and A. Karlsson. On evidential combination rules for ensemble classifiers. In Proceedings of the 11th International Conference on Information Fusion, pages 553–560, 2008.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom08.pdf
  72. U. Johansson, T. Löfström, and H. Boström. The problem with ranking ensembles based on training or validation performance. In Proceedings of the International Joint Conference on Neural Networks, pages 3221–3227. IEEE Press, 2008. Available from: http://people.dsv.su.se/~henke/papers/johansson08c.pdf
  73. U. Johansson, H. Boström, and R. König. Extending nearest neighbor classification with spheres of confidence. In Proceedings of the 21st Florida Artificial Intelligence Research Society Conference, pages 282–287. AAAI Press, 2008. Available from: http://people.dsv.su.se/~henke/papers/johansson08b.pdf
  74. U. Johansson, C. Sönströd, T. Löfström, and H. Boström. Chipper – a novel algorithm for concept description. In Proceedings of the Scandinavian Conference on Artificial Intelligence, pages 133–140. IOS Press, 2008.
    Available from: http: //people.dsv.su.se/~henke/papers/johansson08a.pdf
  75. Boström H., United States Patent no. 7,379,941. Method for Efficiently Checking Coverage of Rules Derived from a Logical Theory. Filed on September 13, 2003, issued May 27, 2008.
  76. H. Boström. Estimating class probabilities in random forests. In Proceedings of the Sixth International Conference on Machine Learning and Applications, pages 211–216. IEEE Computer Society, 2007.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom07c.pdf
  77. S. Deegalla and H. Boström. Classification of microarrays with knn: Comparison of dimensionality reduction methods. In Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning, LNCS 4881, pages 800–809. Springer-Verlag, 2007.
    Available from: http://people.dsv.su.se/~henke/papers/deegalla07.pdf
  78. H. Boström. Feature vs. classifier fusion for predictive data mining – a case study in pesticide classification. In Proceedings of the 10th International Conference on Information Fusion, pages 121–126, 2007.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom07b.pdf
  79. H. Boström. Maximizing the area under the roc curve with decision lists and rule sets. In Proceedings of the SIAM International Conference on Data Mining, pages 27–34, 2007.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom07a.pdf
  80. T. Karunaratne and H. Boström. Using background knowledge for graph based learning: a case study in chemoinformatics. In Proceedings of the 16th International Conference on Inductive Logic Programming, pages 116–118, 2006. Available from: http://people.dsv.su.se/~henke/papers/karunaratne06c.pdf
  81. T. Karunaratne and H. Boström. Learning to classify structured data by graph propositionalization. In Proceedings of the Second IASTED International Conference on Computational Intelligence, pages 393–398, 2006.
    Available from: http://people.dsv.su.se/~henke/papers/karunaratne06b.pdf
  82. S. Deegalla and H. Boström. Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification. In Proceedings of the Fifth International Conference on Machine Learning and Applications, pages 245–250, 2006.
    Available from: http://people.dsv.su.se/~henke/papers/deegalla06.pdf
  83. T. Karunaratne and H. Boström. Learning from structured data by finger printing. In Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence, pages 120–126. IOS Press, 2006.
    Available from: http://people.dsv.su.se/~henke/papers/karunaratne06a.pdf
  84. U. Norinder, P. Liden, and H. Boström. Discrimination between modes of toxic action of phenols using rule based methods. Molecular Diversity, 10(2):207–212, 2006. H. Boström. Maximizing the area under the roc curve using incremental reduced error pruning. In Proceedings of the ICML 2005 Workshop on ROC Analysis in Machine Learning, 2005.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom05.pdf
  85. H. Boström. Pruning and exclusion criteria for unordered incremental reduced error pruning. In Proceedings of the Workshop on Advances in Rule Learning at 15th European Conference on Machine Learning, pages 17–29, 2004.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom04.pdf
  86. T. Lindgren and H. Boström. Resolving rule conflicts with double induction. Intelligent Data Analysis, 8(5):457–468, 2004.
    Available from: http://people.dsv.su.se/~henke/papers/lindgren04.pdf
  87. W. Rao, H. Boström, and S. Xie. Rule induction for structural damage identification. In Proceedings of International Conference on Machine Learning and Cybernetics, pages 2865–2869, 2004.
  88. M. Jacobsson, P. Liden, E. Stjernschantz, H. Boström, and U. Norinder. Improving structure-based virtual screening by multivariate analysis of scoring data. Medicinal Chemistry, 46(26):5781–5789, 2003.
  89. T. Lindgren and H. Boström. Resolving rule conflicts with double induction. In Proceedings of the 5th International Symposium on Intelligent Data Analysis, pages 60–67. Springer, 2003.
    Available from: http://people.dsv.su.se/~henke/papers/lindgren03.pdf
  90. T. Lindgren and H. Boström. Classification with intersecting rules. In Proceedings of the 13th International Conference on Algorithmic Learning Theory, pages 395–402. Springer, 2002.
    Available from: http://people.dsv.su.se/~henke/papers/lindgren02.pdf
  91. P. Liden, L. Asker, and H. Boström. Rule induction for classification of gene expression array data. In Proceedings of Principles of Data Mining and Knowledge Discovery: 6th European Conference, LNAI vol. 2431, pages 338–347. Springer, 2002.
    Available from: http://people.dsv.su.se/~henke/papers/liden02.pdf
  92. M. Eineborg and H. Boström. Classifying uncovered examples by rule stretching. In Proceedings of the Eleventh International Conference on Inductive Logic Programming, volume 2157 of LNAI, pages 41–50. Springer, 2001.
    Available from: http://people.dsv.su.se/~henke/papers/eineborg01.pdf
  93. A. Hulth, J. Karlgren, A. Jonsson, H. Boström, and L. Asker. Automatic keyword extraction using domain knowledge. In Proceedings of Second International Conference on Computational Linguistics and Intelligent Text Processing, LNCS 2004, pages 472–482. Springer, 2001.
    Available from: http://people.dsv.su.se/~henke/papers/hulth01.pdf
  94. M. Huss, H. Boström, L. Asker, and J. Cöster. Learning to recognize brain specific proteins based on low-level features from on-line prediction servers. In Proceedings of BIOKDD-2001: Workshop on Data Mining in Bioinformatics, pages 45–49, 2001.
    Available from: http://people.dsv.su.se/~henke/papers/huss01.pdf
  95. J. J. Rodriguez, C. J. Alonso, and H. Boström. Boosting interval based literals. Intelligent Data Analysis, 5(3):245–262, 2001.
    Available from: http://people.dsv.su.se/~henke/papers/rodriguez01.pdf
  96. H. Boström. Induction of recursive transfer rules. In J. Cussens and S. Dzeroski, editors, Learning Language in Logic, LNAI 1925, pages 237–246. Springer- Verlag, 2001.
    Available from: http://people.dsv.su.se/~henke/papers/bostrom01.pdf
  97. J. J. Rodriguez, C. J. Alonso, and H. Boström. Learning first order logic time series classifiers: Rules and boosting. In Principles of Data Mining and Knowledge Discovery: 4th European Conference, LNAI vol. 1910, pages 299–308. Springer, 2000.
    Available from: http://people.dsv.su.se/~henke/papers/rodriguez00b.pdf
  98. J. J. Rodriguez, C. J. Alonso, and H. Boström. Learning first order logic time series classifiers. In Inductive Logic Programming: 10th International Conference. Work-in-Progress Reports, pages 260–275, 2000.
    Available from: http://people.dsv.su.se/~henke/papers/rodriguez00a.pdf
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U. Johansson, C. Sönströd, T. Löfström and H. Boström. Obtaining accurate and comprehensible classifiers using oracle coaching. Intelligent Data Analysis, vol. 16, no. 2, 2012 (in press)