## Contact

**KTH Royal Institute of Technology**

*SE-100 44 Stockholm Sweden +46 8 790 60 00*

[1]

S. Deegalla *et al.*, "Random subspace and random projection nearest neighbor ensembles for high dimensional data," *Expert systems with applications*, vol. 191, 2022.

[2]

U. Johansson *et al.*, "Rule extraction with guarantees from regression models," *Pattern Recognition*, vol. 126, pp. 108554, 2022.

[3]

H. Linusson, U. Johansson and H. Boström, "Efficient conformal predictor ensembles," *Neurocomputing*, vol. 397, pp. 266-278, 2020.

[4]

R. Aler, J. M. Valls and H. Boström, "Study of Hellinger Distance as a splitting metric for Random Forests in balanced and imbalanced classification datasets," *Expert systems with applications*, vol. 149, 2020.

[5]

U. Johansson *et al.*, "Efficient Venn predictors using random forests," *Machine Learning*, vol. 108, no. 3, pp. 535-550, 2019.

[6]

T. Vasiloudis, G. D. F. Morales and H. Boström, "Quantifying Uncertainty in Online Regression Forests," *Journal of machine learning research*, vol. 20, pp. 1-35, 2019.

[7]

U. Johansson *et al.*, "Interpretable regression trees using conformal prediction," *Expert systems with applications*, vol. 97, pp. 394-404, 2018.

[8]

R. B. Gurung, T. Lindgren and H. Boström, "Learning random forest from histogram data using split specific axis rotation," *International Journal of Machine Learning and Computing*, vol. 8, no. 1, pp. 74-79, 2018.

[9]

H. Boström *et al.*, "Accelerating difficulty estimation for conformal regression forests," *Annals of Mathematics and Artificial Intelligence*, vol. 81, no. 1-2, pp. 125-144, 2017.

[10]

J. Zhao *et al.*, "Learning from heterogeneous temporal data from electronic health records," *Journal of Biomedical Informatics*, vol. 65, pp. 105-119, 2017.

[11]

R. B. Gurung, T. Lindgren and H. Boström, "Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests," *International Journal of Prognostics and Health Management*, vol. 8, no. 1, 2017.

[12]

A. Henriksson *et al.*, "Ensembles of randomized trees using diverse distributed representations of clinical events," *BMC Medical Informatics and Decision Making*, vol. 16, no. 2, 2016.

[13]

I. Karlsson, P. Papapetrou and H. Boström, "Generalized random shapelet forests," *Data mining and knowledge discovery*, vol. 30, no. 5, pp. 1053-1085, 2016.

[14]

T. Löfström *et al.*, "Bias Reduction through Conditional Conformal Prediction," *Intelligent Data Analysis*, vol. 9, no. 6, pp. 1355-1375, 2015.

[15]

J. Zhao *et al.*, "Handling Temporality of Clinical Events for Drug Safety Surveillance," *AMIA Annual Symposium Proceedings*, vol. 2015, pp. 1371-1380, 2015.

[16]

C. Dudas, Amosh. C. Ng and H. Boström, "Post-analysis of multi-objective optimization solutions using decision trees," *Intelligent Data Analysis*, vol. 19, no. 2, pp. 259-278, 2015.

[17]

J. Zhao *et al.*, "Predictive modeling of structured electronic health records for adverse drug event detection," *BMC Medical Informatics and Decision Making*, vol. 15, no. 4, 2015.

[18]

A. Henelius *et al.*, "A peek into the black box : exploring classifiers by randomization," *Data mining and knowledge discovery*, vol. 28, no. 5-6, pp. 1503-1529, 2014.

[19]

C. Dudas *et al.*, "Integration of data mining and multi-objective optimisation for decision support in production system development," *International journal of computer integrated manufacturing (Print)*, vol. 27, no. 9, pp. 824-839, 2014.

[20]

U. Johansson *et al.*, "Regression conformal prediction with random forests," *Machine Learning*, vol. 97, no. 1-2, pp. 155-176, 2014.

[21]

T. Karunaratne, H. Bostrom and U. Norinder, "Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling," *Intelligent Data Analysis*, vol. 17, no. 2, pp. 327-341, 2013.

[22]

O. P. 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 (IJCSEE)*, vol. 1, no. 1, pp. 50-54, 2013.

[23]

U. Norinder and H. Boström, "Representing descriptors derived from multiple conformations as uncertain features for machine learning," *Journal of Molecular Modeling*, vol. 19, no. 6, pp. 2679-2685, 2013.

[24]

H. Boström, "Forests of probability estimation trees," *International journal of pattern recognition and artificial intelligence*, vol. 26, no. 2, 2012.

[25]

U. Norinder and H. Boström, "Introducing Uncertainty in Predictive Modeling-Friend or Foe?," *Journal of Chemical Information and Modeling*, vol. 52, no. 11, pp. 2815-2822, 2012.

[26]

U. Johansson *et al.*, "Obtaining accurate and comprehensible classifiers using oracle coaching," *Intelligent Data Analysis*, vol. 16, no. 2, pp. 247-263, 2012.

[27]

U. Johansson *et al.*, "The Trade-Off between Accuracy and Comprehensibility for Predictive In Silico Modeling," *Future Medicinal Chemistry*, vol. 3, no. 6, pp. 647-663, 2011.

[28]

T. Karunaratne and H. Boström, "DIFFER: A Propositionalization Approach for Learning from Structured Data," *Proceedings of World Academy of Science, Engineering and Technology*, vol. 15, pp. 49-51, 2006.

[29]

U. Norinder, P. Lidén and H. Boström, "Discrimination between modes of toxic action of phenols using rule based methods," *Molecular diversity*, vol. 10, no. 2, pp. 207-212, 2006.

[30]

T. Lindgren and H. Boström, "Resolving rule conflicts with double induction," *Intelligent Data Analysis*, vol. 8, no. 5, pp. 457-468, 2004.

[31]

T. Lindgren and H. Boström, "Resolving rule conflicts with double induction," *Intelligent Data Analysis*, vol. 8, no. 5, pp. 457-468, 2004.

[32]

M. Jacobsson *et al.*, "Improving structure-based virtual screening by multivariate analysis of scoring data," *Journal of Medicinal Chemistry*, vol. 46, no. 26, pp. 5781-5789, 2003.

[33]

A. Alkhatib *et al.*, "Approximating Score-based Explanation Techniques Using Conformal Regression," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 450-469.

[34]

U. Johansson *et al.*, "Confidence Classifiers with Guaranteed Accuracy or Precision," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 513-533.

[35]

S. Ennadir *et al.*, "Conformalized Adversarial Attack Detection for Graph Neural Networks," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 311-323.

[36]

A. Alkhatib, H. Boström and M. Vazirgiannis, "Explaining Predictions by Characteristic Rules," in *Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, Part I*, 2023, pp. 389-403.

[37]

N. Gauraha and H. Boström, "Investigating the Contribution of Privileged Information in Knowledge Transfer LUPI by Explainable Machine Learning," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 470-484.

[38]

H. Boström, H. Linusson and A. Vesterberg, "Mondrian Predictive Systems for Censored Data," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 399-412.

[39]

[40]

T. Löfström *et al.*, "Tutorial on using Conformal Predictive Systems in KNIME," in *Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023*, 2023, pp. 602-620.

[41]

A. Alkhatib, H. Boström and U. Johansson, "Assessing Explanation Quality by Venn Prediction," in *Proceedings of the 11th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2022*, 2022, pp. 42-54.

[42]

N. Xu *et al.*, "Image Keypoint Matching Using Graph Neural Networks," in *Complex Networks & Their Applications X*, 2022, pp. 441-451.

[43]

H. Boström, "crepes : a Python Package for Generating Conformal Regressors and Predictive Systems," in *Proceedings of the 11th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2022*, 2022, pp. 24-41.

[44]

U. Johansson, T. Löfström and H. Boström, "Calibrating Multi-Class Models," in *Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021*, 2021, pp. 111-130.

[45]

H. Werner *et al.*, "Evaluation of Updating Strategies for Conformal Predictive Systems in the Presence of Extreme Events," in *Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021*, 2021, pp. 229-242.

[46]

U. Johansson, H. Boström and T. Löfström, "Investigating Normalized Conformal Regressors," in *2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings*, 2021.

[47]

N. Safinianaini *et al.*, "Orthogonal Mixture of Hidden Markov Models," in *Machine learning and knowledge discovery in databases, ECML PKDD 2020, pt i*, 2021, pp. 509-525.

[48]

N. Safinianaini and H. Boström, "Towards interpretability of Mixtures of Hidden Markov Models," in *Proceedings for the Explainable Agency in AI Workshop at the 35th AAAI Conference on Artificial Intelligence (https://sites.google.com/view/xaiworkshop/)*, 2021.

[49]

U. Johansson, T. Löfström and H. Boström, "Well-Calibrated and Sharp Interpretable Multi-Class Models," in *Conference proceedings : 2021 Modeling Decisions for Artificial Intelligence*, 2021, pp. 193-204.

[50]

L. Karlsson, H. Boström and P. Zieger, "Classification of Aerosol Particles using Inductive Conformal Prediction," in *Proceedings of the 9th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2020*, 2020, pp. 257-268.

[51]

H. Boström *et al.*, "Explaining multivariate time series forecasts : An application to predicting the Swedish GDP?," in *CEUR Workshop Proceedings*, 2020.

[52]

T. Vasiloudis, H. Cho and H. Boström, "Block-distributed Gradient Boosted Trees," in *SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval*, 2019, pp. 1025-1028.

[53]

U. Johansson, T. Löfström and H. Boström, "Calibrating probability estimation trees using Venn-Abers predictors," in *SIAM International Conference on Data Mining, SDM 2019*, 2019, pp. 28-36.

[54]

U. Johansson *et al.*, "Customized interpretable conformal regressors," in *Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019*, 2019, pp. 221-230.

[55]

N. Safinianaini, H. Boström and V. Kaldo, "Gated hidden markov models for early prediction of outcome of internet-based cognitive behavioral therapy," in *17th Conference on Artificial Intelligence in Medicine, AIME 2019*, 2019, pp. 160-169.

[56]

H. Linusson *et al.*, "Classification with Reject Option Using Conformal Prediction," in *Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I*, 2018, pp. 94-105.

[57]

J. Hollmen *et al.*, "Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature," in *11TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS (PETRA 2018)*, 2018, pp. 1-4.

[58]

H. Boström *et al.*, "Conformal prediction using random survival forests," in *Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017*, 2017, pp. 812-817.

[59]

J. Rebane *et al.*, "Learning from Administrative Health Registries," in *SoGood 2017: Data Science for Social Good : Proceedings*, 2017.

[60]

I. Karlsson *et al.*, "Mining disproportional itemsets for characterizing groups of heart failure patients from administrative health records," in *Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments*, 2017, pp. 394-398.

[61]

H. Linusson *et al.*, "On the calibration of aggregated conformal predictors," in *Proceedings of Machine Learning Research : Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden*, 2017, pp. 154-173.

[62]

E. Ahlberg *et al.*, "Using conformal prediction to prioritize compound synthesis in drug discovery," in *Proceedings of Machine Learning Research : Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden*, 2017, pp. 174-184.

[63]

I. Karlsson, P. Papapetrou and H. Boström, "Early Random Shapelet Forest," in *Discovery Science : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings*, 2016, pp. 261-276.

[64]

H. Boström *et al.*, "Evaluation of a variance-based nonconformity measure for regression forests," in *5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016*, 2016, pp. 75-89.

[65]

L. Asker *et al.*, "Identifying Factors for the Effectiveness of Treatment of Heart Failure : A Registry Study," in *IEEE 29th International Symposiumon Computer-Based Medical Systems : CBMS 2016*, 2016.

[66]

R. B. Gurung, T. Lindgren and H. Boström, "Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins," in *Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference*, 2016, pp. 430-435.

[67]

L. Asker, P. Papapetrou and H. Boström, "Learning from Swedish Healthcare Data," in *Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments*, 2016.

[68]

I. Karlsson and H. Boström, "Predicting Adverse Drug Events using Heterogeneous Event Sequences," in *2016 IEEE International Conference on Healthcare Informatics (ICHI)*, 2016, pp. 356-362.

[69]

H. Linusson *et al.*, "Reliable Confidence Predictions Using Conformal Prediction," in *Advances in Knowledge Discovery and Data Mining : 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I*, 2016, pp. 77-88.

[70]

J. Zhao, A. Henriksson and H. Boström, "Cascading Adverse Drug Event Detection in Electronic Health Records," in *2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) : Proceedings*, 2015, pp. 810-817.

[71]

I. Karlsson, P. Papapetrou and H. Boström, "Forests of Randomized Shapelet Trees," in *Statistical Learning and Data Sciences : Proceedings*, 2015, pp. 126-136.

[72]

A. Henelius *et al.*, "GoldenEye++: a Closer Look into the Black Box," in *International Symposium on Statistical Learning and Data Science*, 2015.

[73]

U. Johansson *et al.*, "Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors," in *Statistical Learning and Data Sciences : Third International Symposium, SLDS 2015 Egham, UK, April 20–23, 2015 Proceedings*, 2015, pp. 271-280.

[74]

R. B. Gurung, T. Lindgren and H. Boström, "Learning Decision Trees from Histogram Data," in *Proceedings of the 2015 International Conference on Data Mining : DMIN 2015*, 2015, pp. 139-145.

[75]

A. Henriksson *et al.*, "Modeling Electronic Health Records in Ensembles of Semantic Spaces for Adverse Drug Event Detection," in *2015 IEEE International Conference on Bioinformatics and Biomedicine : Proceedings*, 2015, pp. 343-350.

[76]

A. Henriksson *et al.*, "Modeling Heterogeneous Clinical Sequence Data in Semantic Space for Adverse Drug Event Detection," in *Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics*, 2015, pp. 792-799.

[77]

L. Carlsson *et al.*, "Modifications to p-Values of Conformal Predictors," in *Statistical Learning and Data Sciences : Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings*, 2015, pp. 251-259.

[78]

J. Zhao, A. Henriksson and H. Boström, "Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes," in *2014 IEEE International Conference on Healthcare Informatics : Proceedings*, 2014, pp. 285-293.

[79]

J. Zhao *et al.*, "Detecting Adverse Drug Events with Multiple Representations of Clinical Measurements," in *Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014*, 2014, pp. 536-543.

[80]

H. Linusson *et al.*, "Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers," in *Artificial Intelligence Applications and Innovations*, 2014.

[81]

I. Karlsson and H. Boström, "Handling Sparsity with Random Forests when Predicting Adverse Drug Events from Electronic Health Records," in *IEEE International Conference on Healthcare Informatics (ICHI) : Proceedings*, 2014, pp. 17-22.

[82]

L. Asker *et al.*, "Mining Candidates for Adverse Drug Interactions in Electronic Patient Records," in *PETRA '14 Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, PETRA’14*, 2014.

[83]

U. Johansson *et al.*, "Regression Trees for Streaming Data with Local Performance Guarantees," in *IEEE International Conference on Big Data, 27-30 October, 2014, Washington, DC, USA*, 2014.

[84]

U. Johansson *et al.*, "Rule Extraction with Guaranteed Fidelity," in *Artificial Intelligence Applications and Innovations : Proceedings*, 2014, pp. 281-290.

[85]

K. Jansson, H. Sundell and H. Boström, "gpuRF and gpuERT : efficient and Scalable GPU Algorithms for Decision Tree Ensembles," in *Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International*, 2014, pp. 1612-1621.

[86]

J. Zhao *et al.*, "Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records," in *Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, 2013.

[87]

O. P. Zacarias and H. Boström, "Comparing Support Vector Regression and Random Forests for Predicting Malaria Incidence in Mozambique," in *2013 International Conference on Advances in ICT for Emerging Regions (ICTer)*, 2013, pp. 217-221.

[88]

U. Johansson, H. Boström and T. Löfström, "Conformal Prediction Using Decision Trees," in *IEEE International Conference on Data Mining*, 2013.

[89]

T. Löfström, U. Johansson and H. Boström, "Effective Utilization of Data in Inductive Conformal Prediction," in *Proceedings of the International Joint Conference on Neural Networks 2013*, 2013.

[90]

U. Johansson *et al.*, "Evolved decision trees as conformal predictors," in *2013 IEEE Congress on Evolutionary Computation (CEC)*, 2013, pp. 1794-1801.

[91]

C. Sotomane *et al.*, "Factors Affecting the Use of Data Mining in Mozambique," in *IST-Africa 2013 Conference Proceedings*, 2013.

[92]

O. P. Zacarias and H. Boström, "Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias," in *Advanced Data Mining and Applications : Proceedings, Part II*, 2013, pp. 189-200.

[93]

A. H. C. Ng *et al.*, "Interleaving innovization with evolutionary multi-objective optimization in production system simulation for faster convergence," in *Learning and Intelligent Optimization : 7th International Conference, LION 7, Revised Selected Papers*, 2013, pp. 1-18.

[94]

U. Johansson, T. Löfström and H. Boström, "Overproduce-and-Select : The Grim Reality," in *IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), 16-19 April 2013 , Singapore*, 2013.

[95]

K. Jansson, H. Sundell and H. Boström, "Parallel tree-ensemble algorithms for GPUs using CUDA," in *Sixth Swedish Workshop on Multicore Computing (MCC13), 2013*, 2013.

[96]

I. Karlsson *et al.*, "Predicting Adverse Drug Events by Analyzing Electronic Patient Records," in *Artificial Intelligence in Medicine : 14th Conference on Artificial Intelligence in Medicine, AIME 2013. Proceedings*, 2013, pp. 125-129.

[97]

U. Johansson, T. Löfström and H. Boström, "Random Brains," in *International Joint Conference on Neural Networks, Dallas, TX, USA, August 4-9, 2013.*, 2013.

[98]

C. Sotomane *et al.*, "Short-term Forecasting of Electricity Consumption in Maputo," in *International Conference on Advances in ICT for Emerging Regions (ICTer) - 2013 : Conference Proceedings*, 2013, pp. 132-136.

[99]

O. P. Zacarias and H. Boström, "Strengthening the Health Information System in Mozambique through Malaria Incidence Prediction," in *IST-Africa 2013 Conference Proceedings*, 2013, pp. 1-7.

[100]

T. Karunaratne and H. Boström, "Can frequent itemset mining be efficiently and effectively used for learning from graph data?," in *11th International Conference on Machine Learning and Applications (ICMLA)*, 2012, pp. 409-414.

[101]

S. Deegalla, H. Boström and K. Walgama, "Choice of Dimensionality Reduction Methods for Feature and Classifier Fusion with Nearest Neighbor Classifiers," in *15th International Conference on Information Fusion*, 2012, pp. 875-881.

[102]

H. Boström and H. Dalianis, "De-identifying health records by means of active learning," in *ICML 2012 workshop on Machine Learning for Clinical Data Analysis 2012*, 2012.

[103]

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 *Proceedings of the Third Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2012)*, 2012, pp. 45-48.

[104]

H. Boström, "Concurrent Learning of Large-Scale Random Forests," in *Scandinavian Conference on Artificial Intelligence*, 2011.

[105]

T. Karunaratne and H. Boström, "Use of frequent itemset mining for learning from graphs–what is gained and what is lost?," in *21st International Conference on Inductive Logic Programming (ILP 2011), Windsor Great Park, United Kingdom, 31st July - 3rd August, 2011*, 2011.

[106]

T. Löfström, U. Johansson and H. Boström, "Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks," in *WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010*, 2010.

[107]

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*, 2010.

[108]

C. Sönströd *et al.*, "Pin-Pointing Concept Descriptions," in *2010 IEEE International Conference on Systems Man and Cybernetics (SMC)*, 2010.

[109]

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 *Ninth International Conference on Machine Learning and Applications (ICMLA), 2010 : Proceedings*, 2010, pp. 828-833.

[110]

U. Johansson *et al.*, "Using Feature Selection with Bagging and Rule Extraction in Drug Discovery," in *Advances in Intelligent Decision Technologies, Second KES International Symposium IDT 2010*, 2010.

[111]

T. Löfström, U. Johansson and H. Boström, "Ensemble member selection using multi-objective optimization," in *IEEE Symposium on Computational Intelligence and Data Mining*, 2009, pp. 245-251.

[112]

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*, 2009, pp. 460-465.

[113]

T. Karunaratne and H. Boström, "Graph propositionalization for random forests," in *The Eighth International Conference on Machine Learning and Applications : Proceedings*, 2009, pp. 196-201.

[114]

S. Deegalla and H. Boström, "Improving Fusion of Dimensionality Reduction Methods for Nearest Neighbor Classification," in *8th International Conference on Machine Learning and Applications, ICMLA 2009*, 2009, pp. 771-775.

[115]

C. Dudas, A. Ng and H. Boström, "Information extraction from solution set of simulation-based multi-objective optimization using data mining," in *Proceedings of Industrial Simulation Conference (ISC) 2009*, 2009, pp. 65-69.

[116]

T. Löfström, U. Johansson and H. Boström, "Using Optimized Optimization Criteria in Ensemble Member Selection," in *SWIFT 2008 - Skövde Workshop on Information Fusion Topics*, 2009.

[117]

C. Dudas and H. Boström, "Using Uncertain Chemical and Thermal Data to Predict Product Quality in a Casting Process," in *Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data*, 2009, pp. 57-61.

[118]

H. Boström and U. Norinder, "Utilizing Information on Uncertainty for *In Silico* Modeling using Random Forests," in *Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009)*, 2009, pp. 59-62.

[119]

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 (MFI 2008)*, 2008, pp. 614-619.

[120]

H. Boström, "Calibrating Random Forests," in *Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA'08)*, 2008, pp. 121-126.

[121]

U. Johansson *et al.*, "Chipper - A Novel Algorithm for Concept Description," in *Frontiers in Artificial Intelligence and Applications*, 2008, pp. 133-140.

[122]

C. Sönströd *et al.*, "Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes," in *Proceedings of Seventh International Conference on Machine Learning and Applications*, 2008.

[123]

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*, 2008.

[124]

C. Dudas, A. Ng and H. Boström, "Information Extraction in Manufacturing using Data Mining Techniques," in *Proceedings of Swedish Production Symposium*, 2008.

[125]

C. Dudas, A. Ng and H. Boström, "Knowledge Extraction in Manufacturing using Data Mining Techniques," in *Proceedings of the Swedish Production Symposium 2008, Stockholm, Sweden, November 18-20, 2008*, 2008, p. 8 sidor.

[126]

H. Boström, R. Johansson and A. Karlsson, "On Evidential Combination Rules for Ensemble Classifiers," in *Proceedings of the 11th International Conference on Information Fusion*, 2008, pp. 553-560.

[127]

T. Löfström, U. Johansson and H. Boström, "On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers," in *2008 Seventh International Conference on Machine Learning and Applications*, 2008, pp. 127-132.

[128]

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*, 2008, pp. 3221-3227.

[129]

S. Deegalla and H. Boström, "Classification of Microarrays with kNN : Comparison of Dimensionality Reduction Methods," in *Intelligent Data Engineering and Automated Learning - IDEAL 2007*, 2007, pp. 800-809.

[130]

H. Boström, "Estimating class probabilities in random forests," in *Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007*, 2007, pp. 211-216.

[131]

H. Boström, "Feature vs. classifier fusion for predictive data mining - A case study in pesticide classification," in *FUSION 2007 - 2007 10th International Conference on Information Fusion*, 2007, pp. 1-7.

[132]

H. Boström, "Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets," in *Proceedings of the 7th SIAM International Conference on Data Mining*, 2007, pp. 27-34.

[133]

T. Karunaratne and H. Boström, "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*, 2007, pp. 153-157.

[134]

T. Karunaratne and H. Boström, "An unsupervised approach to substructure discovery for learning from structured data," in *Proceedings of the 8th International InformationTechnology Conference IITC 2006*, 2006.

[135]

T. Karunaratne and H. Boström, "Learning from structured data by finger printing," in *Publications of the Finnish Artificial Intelligence Society*, 2006, pp. 120-126.

[136]

T. Karunaratne and H. Boström, "Learning to Classify Structured Data by Graph Propositionalization," in *Proceedings of the Second IASTED International Conference on Computational Intelligence*, 2006.

[137]

S. Deegalla and H. Boström, "Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification," in *Publications of the Finnish Artificial Intelligence Society*, 2006, pp. 23-30.

[138]

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.

[139]

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*, 2004.

[140]

W. Rao, H. Boström and S. Xie, "Rule induction for structural damage identification," in *Proc. Int. Conf. Mach. Learning Cybernetics*, 2004, pp. 2865-2869.

[141]

A. Hulth *et al.*, "Automatic Keyword Extraction Using Domain Knowledge," in *Computational Linguistics and Intelligent Text Processing, *1st ed. Berlin / Heidelberg : Springer, 2008.

[142]

T. Karunaratne and H. Boström, "The effect of background knowledge in graph-based learning in the chemoinformatics domain," in *Trends in Intelligent Systems and Computer Engineering, *Oscar Castillo, Li Xu, Sio-Iong Ao Ed., : Springer, 2008, pp. 141-153.

[143]

A. Gammerman *et al.*, "Conformal and probabilistic prediction with applications : editorial," *Machine Learning*, vol. 108, no. 3, pp. 379-380, 2019.

[144]

H. Boström *et al.*, "On the Definition of Information Fusion as a Field of Research," Skövde : Institutionen för kommunikation och information, 2007.

[145]

"Preface," , ML Research Press, 2022.

[146]

T. Vasiloudis, G. De Fransisci Morales and H. Boström, "Quantifying Uncertainty in Online Regression Forests," (Manuscript).

[147]

H. Boström, "Method for efficiently checking coverage of rules derived from a logical theory," us 7379941B2 (2008-05-27), 2003.

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