Publikationer av Henrik Boström
Refereegranskade
Artiklar
[1]
U. Johansson, T. Löfström och H. Boström, "Conformal Predictive Distribution Trees," Annals of Mathematics and Artificial Intelligence, 2023.
[2]
S. Deegalla et al., "Random subspace and random projection nearest neighbor ensembles for high dimensional data," Expert systems with applications, vol. 191, 2022.
[3]
U. Johansson et al., "Rule extraction with guarantees from regression models," Pattern Recognition, vol. 126, s. 108554, 2022.
[4]
H. Linusson, U. Johansson och H. Boström, "Efficient conformal predictor ensembles," Neurocomputing, vol. 397, s. 266-278, 2020.
[5]
R. Aler, J. M. Valls och 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.
[6]
U. Johansson et al., "Efficient Venn predictors using random forests," Machine Learning, vol. 108, no. 3, s. 535-550, 2019.
[7]
T. Vasiloudis, G. D. F. Morales och H. Boström, "Quantifying Uncertainty in Online Regression Forests," Journal of machine learning research, vol. 20, s. 1-35, 2019.
[8]
U. Johansson et al., "Interpretable regression trees using conformal prediction," Expert systems with applications, vol. 97, s. 394-404, 2018.
[9]
R. B. Gurung, T. Lindgren och H. Boström, "Learning random forest from histogram data using split specific axis rotation," International Journal of Machine Learning and Computing, vol. 8, no. 1, s. 74-79, 2018.
[10]
H. Boström et al., "Accelerating difficulty estimation for conformal regression forests," Annals of Mathematics and Artificial Intelligence, vol. 81, no. 1-2, s. 125-144, 2017.
[11]
J. Zhao et al., "Learning from heterogeneous temporal data from electronic health records," Journal of Biomedical Informatics, vol. 65, s. 105-119, 2017.
[12]
R. B. Gurung, T. Lindgren och 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.
[13]
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.
[14]
I. Karlsson, P. Papapetrou och H. Boström, "Generalized random shapelet forests," Data mining and knowledge discovery, vol. 30, no. 5, s. 1053-1085, 2016.
[15]
T. Löfström et al., "Bias Reduction through Conditional Conformal Prediction," Intelligent Data Analysis, vol. 9, no. 6, s. 1355-1375, 2015.
[16]
J. Zhao et al., "Handling Temporality of Clinical Events for Drug Safety Surveillance," AMIA Annual Symposium Proceedings, vol. 2015, s. 1371-1380, 2015.
[17]
C. Dudas, Amosh. C. Ng och H. Boström, "Post-analysis of multi-objective optimization solutions using decision trees," Intelligent Data Analysis, vol. 19, no. 2, s. 259-278, 2015.
[18]
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.
[19]
A. Henelius et al., "A peek into the black box : exploring classifiers by randomization," Data mining and knowledge discovery, vol. 28, no. 5-6, s. 1503-1529, 2014.
[20]
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, s. 824-839, 2014.
[21]
U. Johansson et al., "Regression conformal prediction with random forests," Machine Learning, vol. 97, no. 1-2, s. 155-176, 2014.
[22]
T. Karunaratne, H. Bostrom och 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, s. 327-341, 2013.
[23]
O. P. Zacarias och 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, s. 50-54, 2013.
[24]
U. Norinder och H. Boström, "Representing descriptors derived from multiple conformations as uncertain features for machine learning," Journal of Molecular Modeling, vol. 19, no. 6, s. 2679-2685, 2013.
[25]
H. Boström, "Forests of probability estimation trees," International journal of pattern recognition and artificial intelligence, vol. 26, no. 2, 2012.
[26]
U. Norinder och H. Boström, "Introducing Uncertainty in Predictive Modeling-Friend or Foe?," Journal of Chemical Information and Modeling, vol. 52, no. 11, s. 2815-2822, 2012.
[27]
U. Johansson et al., "Obtaining accurate and comprehensible classifiers using oracle coaching," Intelligent Data Analysis, vol. 16, no. 2, s. 247-263, 2012.
[28]
U. Johansson et al., "The Trade-Off between Accuracy and Comprehensibility for Predictive In Silico Modeling," Future Medicinal Chemistry, vol. 3, no. 6, s. 647-663, 2011.
[29]
T. Karunaratne och H. Boström, "DIFFER: A Propositionalization Approach for Learning from Structured Data," Proceedings of World Academy of Science, Engineering and Technology, vol. 15, s. 49-51, 2006.
[30]
U. Norinder, P. Lidén och H. Boström, "Discrimination between modes of toxic action of phenols using rule based methods," Molecular diversity, vol. 10, no. 2, s. 207-212, 2006.
[31]
T. Lindgren och H. Boström, "Resolving rule conflicts with double induction," Intelligent Data Analysis, vol. 8, no. 5, s. 457-468, 2004.
[32]
T. Lindgren och H. Boström, "Resolving rule conflicts with double induction," Intelligent Data Analysis, vol. 8, no. 5, s. 457-468, 2004.
[33]
M. Jacobsson et al., "Improving structure-based virtual screening by multivariate analysis of scoring data," Journal of Medicinal Chemistry, vol. 46, no. 26, s. 5781-5789, 2003.
Konferensbidrag
[34]
S. Ennadir et al., "A Simple and Yet Fairly Effective Defense for Graph Neural Networks," i AAAI Technical Track on Safe, Robust and Responsible AI Track, 2024, s. 21063-21071.
[35]
Y. Abbahaddou et al., "Bounding The Expected Robustness Of Graph Neural Networks Subject To Node Feature Attacks," i 12th International Conference on Learning Representations, ICLR 2024, 2024.
[36]
H. Boström, "Example-Based Explanations of Random Forest Predictions," i Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, 2024, s. 185-196.
[37]
A. H. Akhavan Rahnama, J. Butepage och H. Boström, "Local List-Wise Explanations of LambdaMART," i Explainable Artificial Intelligence - Second World Conference, xAI 2024, Proceedings, 2024, s. 369-392.
[38]
S. Ennadir et al., "UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks," i Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 Volume 1, 2024, s. 100-111.
[39]
A. Alkhatib et al., "Approximating Score-based Explanation Techniques Using Conformal Regression," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 450-469.
[40]
U. Johansson et al., "Confidence Classifiers with Guaranteed Accuracy or Precision," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 513-533.
[41]
S. Ennadir et al., "Conformalized Adversarial Attack Detection for Graph Neural Networks," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 311-323.
[42]
A. Alkhatib, H. Boström och M. Vazirgiannis, "Explaining Predictions by Characteristic Rules," i Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, Part I, 2023, s. 389-403.
[43]
N. Gauraha och H. Boström, "Investigating the Contribution of Privileged Information in Knowledge Transfer LUPI by Explainable Machine Learning," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 470-484.
[44]
H. Boström, H. Linusson och A. Vesterberg, "Mondrian Predictive Systems for Censored Data," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 399-412.
[45]
[46]
T. Löfström et al., "Tutorial on using Conformal Predictive Systems in KNIME," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 602-620.
[47]
A. Alkhatib, H. Boström och U. Johansson, "Assessing Explanation Quality by Venn Prediction," i Proceedings of the 11th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2022, 2022, s. 42-54.
[48]
N. Xu et al., "Image Keypoint Matching Using Graph Neural Networks," i Complex Networks & Their Applications X, 2022, s. 441-451.
[49]
H. Boström, "crepes : a Python Package for Generating Conformal Regressors and Predictive Systems," i Proceedings of the 11th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2022, 2022, s. 24-41.
[50]
U. Johansson, T. Löfström och H. Boström, "Calibrating Multi-Class Models," i Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021, 2021, s. 111-130.
[51]
H. Werner et al., "Evaluation of Updating Strategies for Conformal Predictive Systems in the Presence of Extreme Events," i Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021, 2021, s. 229-242.
[52]
U. Johansson, H. Boström och T. Löfström, "Investigating Normalized Conformal Regressors," i 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings, 2021.
[53]
H. Boström, U. Johansson och T. Löfström, "Mondrian Conformal Predictive Distributions," i Proceedings of the 10th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2021, 2021, s. 24-38.
[54]
N. Safinianaini et al., "Orthogonal Mixture of Hidden Markov Models," i Machine learning and knowledge discovery in databases, ECML PKDD 2020, pt i, 2021, s. 509-525.
[55]
N. Safinianaini och H. Boström, "Towards interpretability of Mixtures of Hidden Markov Models," i Proceedings for the Explainable Agency in AI Workshop at the 35th AAAI Conference on Artificial Intelligence (https://sites.google.com/view/xaiworkshop/), 2021.
[56]
U. Johansson, T. Löfström och H. Boström, "Well-Calibrated and Sharp Interpretable Multi-Class Models," i Conference proceedings : 2021 Modeling Decisions for Artificial Intelligence, 2021, s. 193-204.
[57]
L. Karlsson, H. Boström och P. Zieger, "Classification of Aerosol Particles using Inductive Conformal Prediction," i Proceedings of the 9th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2020, 2020, s. 257-268.
[58]
H. Werner et al., "Evaluating Different Approaches to Calibrating Conformal Predictive Systems," i Proceedings of the 9th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2020, 2020, s. 134-150.
[59]
H. Boström et al., "Explaining multivariate time series forecasts : An application to predicting the Swedish GDP?," i CEUR Workshop Proceedings, 2020.
[60]
H. Boström och U. Johansson, "Mondrian Conformal Regressors," i Proceedings of the 9th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2020, 2020, s. 114-133.
[61]
T. Vasiloudis, H. Cho och H. Boström, "Block-distributed Gradient Boosted Trees," i SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, s. 1025-1028.
[62]
U. Johansson, T. Löfström och H. Boström, "Calibrating probability estimation trees using Venn-Abers predictors," i SIAM International Conference on Data Mining, SDM 2019, 2019, s. 28-36.
[63]
U. Johansson et al., "Customized interpretable conformal regressors," i Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, 2019, s. 221-230.
[64]
N. Safinianaini, H. Boström och V. Kaldo, "Gated hidden markov models for early prediction of outcome of internet-based cognitive behavioral therapy," i 17th Conference on Artificial Intelligence in Medicine, AIME 2019, 2019, s. 160-169.
[65]
U. Johansson et al., "Interpretable and Specialized Conformal Predictors," i Proceedings of the 8th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2019, 2019, s. 3-22.
[66]
H. Boström, U. Johansson och A. Vesterberg, "Predicting with Confidence from Survival Data," i Proceedings of the 8th Symposium on Conformal and Probabilistic Prediction and Applications, COPA 2019, 2019, s. 123-141.
[67]
H. Linusson et al., "Classification with Reject Option Using Conformal Prediction," i Advances in Knowledge Discovery and Data Mining, PAKDD 2018, PT I, 2018, s. 94-105.
[68]
J. Hollmen et al., "Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature," i 11TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS (PETRA 2018), 2018, s. 1-4.
[69]
U. Johansson et al., "Venn Predictors for Well-Calibrated Probability Estimation Trees," i Proceedings of the 7th Workshop on Conformal and Probabilistic Prediction and Applications, COPA 2018, 2018, s. 3-14.
[70]
H. Boström et al., "Conformal prediction using random survival forests," i Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2017, s. 812-817.
[71]
J. Rebane et al., "Learning from Administrative Health Registries," i SoGood 2017: Data Science for Social Good : Proceedings, 2017.
[72]
I. Karlsson et al., "Mining disproportional itemsets for characterizing groups of heart failure patients from administrative health records," i Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, 2017, s. 394-398.
[73]
H. Linusson et al., "On the calibration of aggregated conformal predictors," i Proceedings of Machine Learning Research : Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden, 2017, s. 154-173.
[74]
E. Ahlberg et al., "Using conformal prediction to prioritize compound synthesis in drug discovery," i Proceedings of Machine Learning Research : Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden, 2017, s. 174-184.
[75]
I. Karlsson, P. Papapetrou och H. Boström, "Early Random Shapelet Forest," i Discovery Science : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings, 2016, s. 261-276.
[76]
H. Boström et al., "Evaluation of a variance-based nonconformity measure for regression forests," i 5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016, 2016, s. 75-89.
[77]
L. Asker et al., "Identifying Factors for the Effectiveness of Treatment of Heart Failure : A Registry Study," i IEEE 29th International Symposiumon Computer-Based Medical Systems : CBMS 2016, 2016.
[78]
R. B. Gurung, T. Lindgren och H. Boström, "Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins," i Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, 2016, s. 430-435.
[79]
L. Asker, P. Papapetrou och H. Boström, "Learning from Swedish Healthcare Data," i Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2016.
[80]
I. Karlsson och H. Boström, "Predicting Adverse Drug Events using Heterogeneous Event Sequences," i 2016 IEEE International Conference on Healthcare Informatics (ICHI), 2016, s. 356-362.
[81]
H. Linusson et al., "Reliable Confidence Predictions Using Conformal Prediction," i Advances in Knowledge Discovery and Data Mining : 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, 2016, Proceedings, Part I, 2016, s. 77-88.
[82]
J. Zhao, A. Henriksson och H. Boström, "Cascading Adverse Drug Event Detection in Electronic Health Records," i 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) : Proceedings, 2015, s. 810-817.
[83]
I. Karlsson, P. Papapetrou och H. Boström, "Forests of Randomized Shapelet Trees," i Statistical Learning and Data Sciences : Proceedings, 2015, s. 126-136.
[84]
A. Henelius et al., "GoldenEye++: a Closer Look into the Black Box," i International Symposium on Statistical Learning and Data Science, 2015.
[85]
U. Johansson et al., "Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors," i Statistical Learning and Data Sciences : Third International Symposium, SLDS 2015 Egham, UK, April 20–23, 2015 Proceedings, 2015, s. 271-280.
[86]
R. B. Gurung, T. Lindgren och H. Boström, "Learning Decision Trees from Histogram Data," i Proceedings of the 2015 International Conference on Data Mining : DMIN 2015, 2015, s. 139-145.
[87]
A. Henriksson et al., "Modeling Electronic Health Records in Ensembles of Semantic Spaces for Adverse Drug Event Detection," i 2015 IEEE International Conference on Bioinformatics and Biomedicine : Proceedings, 2015, s. 343-350.
[88]
A. Henriksson et al., "Modeling Heterogeneous Clinical Sequence Data in Semantic Space for Adverse Drug Event Detection," i Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015, s. 792-799.
[89]
L. Carlsson et al., "Modifications to p-Values of Conformal Predictors," i Statistical Learning and Data Sciences : Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings, 2015, s. 251-259.
[90]
J. Zhao, A. Henriksson och H. Boström, "Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes," i 2014 IEEE International Conference on Healthcare Informatics : Proceedings, 2014, s. 285-293.
[91]
J. Zhao et al., "Detecting Adverse Drug Events with Multiple Representations of Clinical Measurements," i Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, 2014, s. 536-543.
[92]
H. Linusson et al., "Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers," i Artificial Intelligence Applications and Innovations, 2014.
[93]
I. Karlsson och H. Boström, "Handling Sparsity with Random Forests when Predicting Adverse Drug Events from Electronic Health Records," i IEEE International Conference on Healthcare Informatics (ICHI) : Proceedings, 2014, s. 17-22.
[94]
L. Asker et al., "Mining Candidates for Adverse Drug Interactions in Electronic Patient Records," i PETRA '14 Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, PETRA’14, 2014.
[95]
U. Johansson et al., "Regression Trees for Streaming Data with Local Performance Guarantees," i IEEE International Conference on Big Data, 27-30 October, 2014, Washington, DC, USA, 2014.
[96]
U. Johansson et al., "Rule Extraction with Guaranteed Fidelity," i Artificial Intelligence Applications and Innovations : Proceedings, 2014, s. 281-290.
[97]
K. Jansson, H. Sundell och H. Boström, "gpuRF and gpuERT : efficient and Scalable GPU Algorithms for Decision Tree Ensembles," i Parallel & Distributed Processing Symposium Workshops (IPDPSW), 2014 IEEE International, 2014, s. 1612-1621.
[98]
J. Zhao et al., "Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records," i Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013.
[99]
O. P. Zacarias och H. Boström, "Comparing Support Vector Regression and Random Forests for Predicting Malaria Incidence in Mozambique," i 2013 International Conference on Advances in ICT for Emerging Regions (ICTer), 2013, s. 217-221.
[100]
U. Johansson, H. Boström och T. Löfström, "Conformal Prediction Using Decision Trees," i IEEE International Conference on Data Mining, 2013.
[101]
T. Löfström, U. Johansson och H. Boström, "Effective Utilization of Data in Inductive Conformal Prediction," i Proceedings of the International Joint Conference on Neural Networks 2013, 2013.
[102]
U. Johansson et al., "Evolved decision trees as conformal predictors," i 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, s. 1794-1801.
[103]
C. Sotomane et al., "Factors Affecting the Use of Data Mining in Mozambique," i IST-Africa 2013 Conference Proceedings, 2013.
[104]
O. P. Zacarias och H. Boström, "Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias," i Advanced Data Mining and Applications : Proceedings, Part II, 2013, s. 189-200.
[105]
A. H. C. Ng et al., "Interleaving innovization with evolutionary multi-objective optimization in production system simulation for faster convergence," i Learning and Intelligent Optimization : 7th International Conference, LION 7, Revised Selected Papers, 2013, s. 1-18.
[106]
U. Johansson, T. Löfström och H. Boström, "Overproduce-and-Select : The Grim Reality," i IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), 16-19 April 2013 , Singapore, 2013.
[107]
K. Jansson, H. Sundell och H. Boström, "Parallel tree-ensemble algorithms for GPUs using CUDA," i Sixth Swedish Workshop on Multicore Computing (MCC13), 2013, 2013.
[108]
I. Karlsson et al., "Predicting Adverse Drug Events by Analyzing Electronic Patient Records," i Artificial Intelligence in Medicine : 14th Conference on Artificial Intelligence in Medicine, AIME 2013. Proceedings, 2013, s. 125-129.
[109]
U. Johansson, T. Löfström och H. Boström, "Random Brains," i International Joint Conference on Neural Networks, Dallas, TX, USA, August 4-9, 2013., 2013.
[110]
C. Sotomane et al., "Short-term Forecasting of Electricity Consumption in Maputo," i International Conference on Advances in ICT for Emerging Regions (ICTer) - 2013 : Conference Proceedings, 2013, s. 132-136.
[111]
O. P. Zacarias och H. Boström, "Strengthening the Health Information System in Mozambique through Malaria Incidence Prediction," i IST-Africa 2013 Conference Proceedings, 2013, s. 1-7.
[112]
T. Karunaratne och H. Boström, "Can frequent itemset mining be efficiently and effectively used for learning from graph data?," i 11th International Conference on Machine Learning and Applications (ICMLA), 2012, s. 409-414.
[113]
S. Deegalla, H. Boström och K. Walgama, "Choice of Dimensionality Reduction Methods for Feature and Classifier Fusion with Nearest Neighbor Classifiers," i 15th International Conference on Information Fusion, 2012, s. 875-881.
[114]
H. Boström och H. Dalianis, "De-identifying health records by means of active learning," i ICML 2012 workshop on Machine Learning for Clinical Data Analysis 2012, 2012.
[115]
H. Dalianis och H. Boström, "Releasing a Swedish Clinical Corpus after Removing all Words – De-identification Experiments with Conditional Random Fields and Random Forests," i Proceedings of the Third Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2012), 2012, s. 45-48.
[116]
H. Boström, "Concurrent Learning of Large-Scale Random Forests," i Scandinavian Conference on Artificial Intelligence, 2011.
[117]
T. Karunaratne och H. Boström, "Use of frequent itemset mining for learning from graphs–what is gained and what is lost?," i 21st International Conference on Inductive Logic Programming (ILP 2011), Windsor Great Park, United Kingdom, 31st July - 3rd August, 2011, 2011.
[118]
T. Löfström, U. Johansson och H. Boström, "Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks," i WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010, 2010.
[119]
T. Löfström, U. Johansson och H. Boström, "Implicit vs. Explicit Methods for Generating Diverse Ensembles of Artificial Neural Networks," i WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010, 2010.
[120]
C. Sönströd et al., "Pin-Pointing Concept Descriptions," i 2010 IEEE International Conference on Systems Man and Cybernetics (SMC), 2010.
[121]
T. Karunaratne, H. Boström och U. Norinder, "Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - a Case Study with Medicinal Chemistry Datasets," i Ninth International Conference on Machine Learning and Applications (ICMLA), 2010 : Proceedings, 2010, s. 828-833.
[122]
U. Johansson et al., "Using Feature Selection with Bagging and Rule Extraction in Drug Discovery," i Advances in Intelligent Decision Technologies, Second KES International Symposium IDT 2010, 2010.
[123]
T. Löfström, U. Johansson och H. Boström, "Ensemble member selection using multi-objective optimization," i IEEE Symposium on Computational Intelligence and Data Mining, 2009, s. 245-251.
[124]
S. Deegalla och H. Boström, "Fusion of Dimensionality Reduction Methods : a Case Study in Microarray Classification," i Proceedings of the 12th International Conference on Information Fusion, 2009, s. 460-465.
[125]
T. Karunaratne och H. Boström, "Graph propositionalization for random forests," i The Eighth International Conference on Machine Learning and Applications : Proceedings, 2009, s. 196-201.
[126]
S. Deegalla och H. Boström, "Improving Fusion of Dimensionality Reduction Methods for Nearest Neighbor Classification," i 8th International Conference on Machine Learning and Applications, ICMLA 2009, 2009, s. 771-775.
[127]
C. Dudas, A. Ng och H. Boström, "Information extraction from solution set of simulation-based multi-objective optimization using data mining," i Proceedings of Industrial Simulation Conference (ISC) 2009, 2009, s. 65-69.
[128]
T. Löfström, U. Johansson och H. Boström, "Using Optimized Optimization Criteria in Ensemble Member Selection," i SWIFT 2008 - Skövde Workshop on Information Fusion Topics, 2009.
[129]
C. Dudas och H. Boström, "Using Uncertain Chemical and Thermal Data to Predict Product Quality in a Casting Process," i Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data, 2009, s. 57-61.
[130]
H. Boström och U. Norinder, "Utilizing Information on Uncertainty for In Silico Modeling using Random Forests," i Proceedings of the 3rd Skövde Workshop on Information Fusion Topics (SWIFT 2009), 2009, s. 59-62.
[131]
R. Johansson, H. Boström och A. Karlsson, "A Study on Class-Specifically Discounted Belief for Ensemble Classifiers," i Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), 2008, s. 614-619.
[132]
H. Boström, "Calibrating Random Forests," i Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA'08), 2008, s. 121-126.
[133]
U. Johansson et al., "Chipper - A Novel Algorithm for Concept Description," i Frontiers in Artificial Intelligence and Applications, 2008, s. 133-140.
[134]
C. Sönströd et al., "Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes," i Proceedings of Seventh International Conference on Machine Learning and Applications, 2008.
[135]
U. Johansson, H. Boström och R. König, "Extending Nearest Neighbor Classification with Spheres of Confidence," i Proceedings of the 21st Florida Artificial Intelligence Research Society Conference, 2008.
[136]
C. Dudas, A. Ng och H. Boström, "Information Extraction in Manufacturing using Data Mining Techniques," i Proceedings of Swedish Production Symposium, 2008.
[137]
C. Dudas, A. Ng och H. Boström, "Knowledge Extraction in Manufacturing using Data Mining Techniques," i Proceedings of the Swedish Production Symposium 2008, Stockholm, Sweden, November 18-20, 2008, 2008, s. 8 sidor.
[138]
H. Boström, R. Johansson och A. Karlsson, "On Evidential Combination Rules for Ensemble Classifiers," i Proceedings of the 11th International Conference on Information Fusion, 2008, s. 553-560.
[139]
T. Löfström, U. Johansson och H. Boström, "On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers," i 2008 Seventh International Conference on Machine Learning and Applications, 2008, s. 127-132.
[140]
U. Johansson, T. Löfström och H. Boström, "The Problem with Ranking Ensembles Based on Training or Validation Performance," i Proceedings of the International Joint Conference on Neural Networks, 2008, s. 3221-3227.
[141]
S. Deegalla och H. Boström, "Classification of Microarrays with kNN : Comparison of Dimensionality Reduction Methods," i Intelligent Data Engineering and Automated Learning - IDEAL 2007, 2007, s. 800-809.
[142]
H. Boström, "Estimating class probabilities in random forests," i Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007, 2007, s. 211-216.
[143]
H. Boström, "Feature vs. classifier fusion for predictive data mining - A case study in pesticide classification," i FUSION 2007 - 2007 10th International Conference on Information Fusion, 2007, s. 1-7.
[144]
H. Boström, "Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets," i Proceedings of the 7th SIAM International Conference on Data Mining, 2007, s. 27-34.
[145]
T. Karunaratne och H. Boström, "Using background knowledge for graph based learning : a case study in chemoinformatics," i IMECS 2007: International Multiconference of Engineers and Computer Scientists, Vols I and II, 2007, s. 153-157.
[146]
T. Karunaratne och H. Boström, "An unsupervised approach to substructure discovery for learning from structured data," i Proceedings of the 8th International InformationTechnology Conference IITC 2006, 2006.
[147]
T. Karunaratne och H. Boström, "Learning from structured data by finger printing," i Publications of the Finnish Artificial Intelligence Society, 2006, s. 120-126.
[148]
T. Karunaratne och H. Boström, "Learning to Classify Structured Data by Graph Propositionalization," i Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006.
[149]
S. Deegalla och H. Boström, "Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification," i Publications of the Finnish Artificial Intelligence Society, 2006, s. 23-30.
[150]
H. Boström, "Maximizing the Area under the ROC Curve using Incremental Reduced Error Pruning," i Proceedings of the ICML 2005 Workshop on ROC Analysis in Machine Learning, 2005.
[151]
H. Boström, "Pruning and Exclusion Criteria for Unordered Incremental Reduced Error Pruning," i Proceedings of the Workshop on Advances in Rule Learning at 15th European Conference on Machine Learning, 2004.
[152]
W. Rao, H. Boström och S. Xie, "Rule induction for structural damage identification," i Proc. Int. Conf. Mach. Learning Cybernetics, 2004, s. 2865-2869.
Kapitel i böcker
[153]
A. Hulth et al., "Automatic Keyword Extraction Using Domain Knowledge," i Computational Linguistics and Intelligent Text Processing, 1. uppl. Berlin / Heidelberg : Springer, 2008.
[154]
T. Karunaratne och H. Boström, "The effect of background knowledge in graph-based learning in the chemoinformatics domain," i Trends in Intelligent Systems and Computer Engineering, Oscar Castillo, Li Xu, Sio-Iong Ao red., : Springer, 2008, s. 141-153.
Icke refereegranskade
Artiklar
[155]
A. Gammerman et al., "Conformal and probabilistic prediction with applications : editorial," Machine Learning, vol. 108, no. 3, s. 379-380, 2019.
Rapporter
[156]
H. Boström et al., "On the Definition of Information Fusion as a Field of Research," Skövde : Institutionen för kommunikation och information, 2007.
Proceedings (redaktörskap)
[157]
"Preface," , ML Research Press, 2022.
Övriga
[158]
T. Vasiloudis, G. De Fransisci Morales och H. Boström, "Quantifying Uncertainty in Online Regression Forests," (Manuskript).
Patent
Patent
[159]
H. Boström, "Method for efficiently checking coverage of rules derived from a logical theory," us 7379941B2 (2008-05-27), 2003.
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2024-11-17 03:43:04