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ORCIDHenrik Boström
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- affiliation: Stockholm University
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2020 – today
- 2024
[j28]Amir Hossein Akhavan Rahnama
, Judith Bütepage, Pierre Geurts, Henrik Boström:
Can local explanation techniques explain linear additive models? Data Min. Knowl. Discov. 38(1): 237-280 (2024)- [c120]Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström:
A Simple and Yet Fairly Effective Defense for Graph Neural Networks. AAAI 2024: 21063-21071 - [c119]Amr Alkhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis:
Interpretable Graph Neural Networks for Tabular Data. ECAI 2024: 1848-1855 - [c118]Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström:
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks. ICLR 2024 - [c117]Henrik Boström:
Example-Based Explanations of Random Forest Predictions. IDA (2) 2024: 185-196 - [c116]Amr Alkhatib, Henrik Boström:
Fast Approximation of Shapley Values with Limited Data. SCAI 2024: 95-100 - [c115]Amir Hossein Akhavan Rahnama, Judith Bütepage, Henrik Boström:
Local Point-wise Explanations of LambdaMART. SCAI 2024: 121-130 - [c114]Amir Hossein Akhavan Rahnama, Judith Bütepage, Henrik Boström:
Local List-Wise Explanations of LambdaMART. xAI (2) 2024: 369-392 - [i13]Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström:
A Simple and Yet Fairly Effective Defense for Graph Neural Networks. CoRR abs/2402.13987 (2024) - [i12]Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis, Henrik Boström:
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks. CoRR abs/2404.17947 (2024) - [i11]Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Explaining Predictions by Characteristic Rules. CoRR abs/2405.21003 (2024) - [i10]Amr Alkhatib, Henrik Boström:
Interpretable Graph Neural Networks for Heterogeneous Tabular Data. CoRR abs/2408.07661 (2024) - 2023
- [c113]Sofiane Ennadir, Amr Alkhatib, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström:
UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks. COMPLEX NETWORKS (1) 2023: 100-111 - [c112]Sofiane Ennadir, Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Conformalized Adversarial Attack Detection for Graph Neural Networks. COPA 2023: 311-323 - [c111]Henrik Boström, Henrik Linusson, Anders Vesterberg:
Mondrian Predictive Systems for Censored Data. COPA 2023: 399-412 - [c110]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. COPA 2023: 450-469 - [c109]Niharika Gauraha, Henrik Boström:
Investigating the Contribution of Privileged Information in Knowledge Transfer LUPI by Explainable Machine Learning. COPA 2023: 470-484 - [c108]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Confidence Classifiers with Guaranteed Accuracy or Precision. COPA 2023: 513-533 - [c107]Tuwe Löfström, Alexander Bondaletov, Artem Ryasik, Henrik Boström, Ulf Johansson:
Tutorial on using Conformal Predictive Systems in KNIME. COPA 2023: 602-620 - [e3]Harris Papadopoulos, Khuong An Nguyen, Henrik Boström, Lars Carlsson:
Conformal and Probabilistic Prediction with Applications, 13-15 September 2023, Limassol, Cyprus. Proceedings of Machine Learning Research 204, PMLR 2023 [contents] - [i9]Amr Alkhatib, Sofiane Ennadir, Henrik Boström, Michalis Vazirgiannis:
Interpretable Graph Neural Networks for Tabular Data. CoRR abs/2308.08945 (2023) - [i8]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. CoRR abs/2308.11975 (2023) - [i7]Henrik Boström:
Example-Based Explanations of Random Forest Predictions. CoRR abs/2311.14581 (2023) - 2022
- [j27]Sampath Deegalla, Keerthi Walgama, Panagiotis Papapetrou, Henrik Boström:
Random subspace and random projection nearest neighbor ensembles for high dimensional data. Expert Syst. Appl. 191: 116078 (2022) - [j26]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Rule extraction with guarantees from regression models. Pattern Recognit. 126: 108554 (2022) - [c106]Ulf Johansson, Henrik Boström, Khuong An Nguyen, Zhiyuan Luo, Lars Carlsson:
Preface. COPA 2022: 1-3 - [c105]Henrik Boström:
crepes: a Python Package for Generating Conformal Regressors and Predictive Systems. COPA 2022: 24-41 - [c104]Amr Alkhatib, Henrik Boström, Ulf Johansson:
Assessing Explanation Quality by Venn Prediction. COPA 2022: 42-54 - [c103]Amr Alkhatib, Henrik Boström, Michalis Vazirgiannis:
Explaining Predictions by Characteristic Rules. ECML/PKDD (1) 2022: 389-403 - [e2]Ulf Johansson, Henrik Boström, Khuong An Nguyen, Zhiyuan Luo, Lars Carlsson:
Conformal and Probabilistic Prediction with Applications, 24-26 August 2022, Brighton, UK. Proceedings of Machine Learning Research 179, PMLR 2022 [contents] - [i6]Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström:
Image Keypoint Matching using Graph Neural Networks. CoRR abs/2205.14275 (2022) - 2021
- [c102]Nancy Xu, Giannis Nikolentzos, Michalis Vazirgiannis, Henrik Boström:
Image Keypoint Matching Using Graph Neural Networks. COMPLEX NETWORKS 2021: 441-451 - [c101]Henrik Boström, Ulf Johansson, Tuwe Löfström:
Mondrian conformal predictive distributions. COPA 2021: 24-38 - [c100]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Calibrating multi-class models. COPA 2021: 111-130 - [c99]Hugo Werner, Lars Carlsson, Ernst Ahlberg, Henrik Boström:
Evaluation of updating strategies for conformal predictive systems in the presence of extreme events. COPA 2021: 229-242 - [c98]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Well-Calibrated and Sharp Interpretable Multi-Class Models. MDAI 2021: 193-204 - [c97]Ulf Johansson, Henrik Boström, Tuwe Löfström:
Investigating Normalized Conformal Regressors. SSCI 2021: 1-8 - [i5]Negar Safinianaini, Henrik Boström:
Towards interpretability of Mixtures of Hidden Markov Models. CoRR abs/2103.12576 (2021) - [i4]Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström:
Evaluation of Local Model-Agnostic Explanations Using Ground Truth. CoRR abs/2106.02488 (2021) - 2020
- [j25]Ricardo Aler, José María Valls, Henrik Boström:
Study of Hellinger Distance as a splitting metric for Random Forests in balanced and imbalanced classification datasets. Expert Syst. Appl. 149: 113264 (2020) - [j24]Henrik Linusson, Ulf Johansson, Henrik Boström:
Efficient conformal predictor ensembles. Neurocomputing 397: 266-278 (2020) - [j23]Jing Zhao, Panagiotis Papapetrou, Lars Asker, Henrik Boström:
Corrigendum to 'Learning from heterogeneous temporal data in electronic health records'. [J. Biomed. Inform. 65 (2017) 105-119]. J. Biomed. Informatics 101: 103352 (2020) - [c96]Henrik Boström, Ulf Johansson:
Mondrian conformal regressors. COPA 2020: 114-133 - [c95]Hugo Werner, Lars Carlsson, Ernst Ahlberg, Henrik Boström:
Evaluating different approaches to calibrating conformal predictive systems. COPA 2020: 134-150 - [c94]Linn Karlsson, Henrik Boström, Paul Zieger:
Classication of aerosol particles using inductive conformal prediction. COPA 2020: 257-268 - [c93]Henrik Boström, Peter Höglund, Sven-Olof Junker, Ann-Sofie Öberg, Martin Sparr:
Explaining Multivariate Time Series Forecasts: An Application to Predicting the Swedish GDP. XI-ML@KI 2020 - [c92]Negar Safinianaini, Camila P. E. de Souza, Henrik Boström, Jens Lagergren:
Orthogonal Mixture of Hidden Markov Models. ECML/PKDD (1) 2020: 509-525
2010 – 2019
- 2019
- [j22]Theodore Vasiloudis, Gianmarco De Francisci Morales, Henrik Boström:
Quantifying Uncertainty in Online Regression Forests. J. Mach. Learn. Res. 20: 155:1-155:35 (2019) - [j21]Alexander Gammerman, Vladimir Vovk, Henrik Boström, Lars Carlsson:
Conformal and probabilistic prediction with applications: editorial. Mach. Learn. 108(3): 379-380 (2019) - [j20]Ulf Johansson, Tuve Löfström, Henrik Linusson, Henrik Boström:
Efficient Venn predictors using random forests. Mach. Learn. 108(3): 535-550 (2019) - [c91]Negar Safinianaini, Henrik Boström, Viktor Kaldo:
Gated Hidden Markov Models for Early Prediction of Outcome of Internet-Based Cognitive Behavioral Therapy. AIME 2019: 160-169 - [c90]Ulf Johansson, Tuwe Löfström, Henrik Boström, Cecilia Sönströd:
Interpretable and specialized conformal predictors. COPA 2019: 3-22 - [c89]Henrik Boström, Ulf Johansson, Anders Vesterberg:
Predicting with Confidence from Survival Data. COPA 2019: 123-141 - [c88]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Customized Interpretable Conformal Regressors. DSAA 2019: 221-230 - [c87]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Calibrating Probability Estimation Trees using Venn-Abers Predictors. SDM 2019: 28-36 - [c86]Theodore Vasiloudis, Hyunsu Cho, Henrik Boström:
Block-distributed Gradient Boosted Trees. SIGIR 2019: 1025-1028 - [i3]Theodore Vasiloudis, Hyunsu Cho, Henrik Boström:
Block-distributed Gradient Boosted Trees. CoRR abs/1904.10522 (2019) - [i2]Amir Hossein Akhavan Rahnama, Henrik Boström:
A study of data and label shift in the LIME framework. CoRR abs/1910.14421 (2019) - 2018
- [j19]Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström:
Interpretable regression trees using conformal prediction. Expert Syst. Appl. 97: 394-404 (2018) - [c85]Ulf Johansson, Tuwe Löfström, Håkan Sundell, Henrik Linusson, Anders Gidenstam, Henrik Boström:
Venn predictors for well-calibrated probability estimation trees. COPA 2018: 3-14 - [c84]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Classification with Reject Option Using Conformal Prediction. PAKDD (1) 2018: 94-105 - [c83]Jaakko Hollmén, Lars Asker, Isak Karlsson, Panagiotis Papapetrou, Henrik Boström, Birgitta Norstedt Wikner, Inger Öhman:
Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature. PETRA 2018: 1-4 - 2017
- [j18]Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson:
Accelerating difficulty estimation for conformal regression forests. Ann. Math. Artif. Intell. 81(1-2): 125-144 (2017) - [j17]Jing Zhao, Panagiotis Papapetrou, Lars Asker, Henrik Boström:
Learning from heterogeneous temporal data in electronic health records. J. Biomed. Informatics 65: 105-119 (2017) - [c82]Henrik Linusson, Ulf Norinder, Henrik Boström, Ulf Johansson, Tuve Löfström:
On the Calibration of Aggregated Conformal Predictors. COPA 2017: 154-173 - [c81]Ernst Ahlberg, Susanne Winiwarter, Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Norinder, Ulf Johansson, Ola Engkvist, Oscar Hammar, Claus Bendtsen, Lars Carlsson:
Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery. COPA 2017: 174-184 - [c80]Henrik Boström, Lars Asker, Ram B. Gurung, Isak Karlsson, Tony Lindgren, Panagiotis Papapetrou:
Conformal Prediction Using Random Survival Forests. ICMLA 2017: 812-817 - [c79]Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström:
Model-agnostic nonconformity functions for conformal classification. IJCNN 2017: 2072-2079 - [c78]Isak Karlsson, Panagiotis Papapetrou, Lars Asker, Henrik Boström, Hans E. Persson:
Mining disproportional itemsets for characterizing groups of heart failure patients from administrative health records. PETRA 2017: 394-398 - 2016
- [j16]Isak Karlsson, Panagiotis Papapetrou, Henrik Boström:
Generalized random shapelet forests. Data Min. Knowl. Discov. 30(5): 1053-1085 (2016) - [j15]Aron Henriksson, Jing Zhao, Hercules Dalianis, Henrik Boström:
Ensembles of randomized trees using diverse distributed representations of clinical events. BMC Medical Informatics Decis. Mak. 16(S-2): 69 (2016) - [c77]Lars Asker, Henrik Boström, Panagiotis Papapetrou, Hans E. Persson:
Identifying Factors for the Effectiveness of Treatment of Heart Failure: A Registry Study. CBMS 2016: 205-206 - [c76]Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson:
Evaluation of a Variance-Based Nonconformity Measure for Regression Forests. COPA 2016: 75-89 - [c75]Isak Karlsson, Panagiotis Papapetrou, Henrik Boström:
Early Random Shapelet Forest. DS 2016: 261-276 - [c74]Ram B. Gurung, Tony Lindgren, Henrik Boström:
Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins. FLAIRS 2016: 430-435 - [c73]Isak Karlsson, Henrik Boström:
Predicting Adverse Drug Events Using Heterogeneous Event Sequences. ICHI 2016: 356-362 - [c72]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Reliable Confidence Predictions Using Conformal Prediction. PAKDD (1) 2016: 77-88 - [c71]Lars Asker, Panagiotis Papapetrou, Henrik Boström:
Learning from Swedish Healthcare Data. PETRA 2016: 47 - [e1]Henrik Boström, Arno J. Knobbe, Carlos Soares, Panagiotis Papapetrou:
Advances in Intelligent Data Analysis XV - 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings. Lecture Notes in Computer Science 9897, 2016, ISBN 978-3-319-46348-3 [contents] - [i1]Andreas Henelius, Kai Puolamäki, Henrik Boström, Panagiotis Papapetrou:
Clustering with Confidence: Finding Clusters with Statistical Guarantees. CoRR abs/1612.08714 (2016) - 2015
- [j14]Catarina Dudas, Amos H. C. Ng, Henrik Boström:
Post-analysis of multi-objective optimization solutions using decision trees. Intell. Data Anal. 19(2): 259-278 (2015) - [j13]Tuve Löfström, Henrik Boström, Henrik Linusson, Ulf Johansson:
Bias reduction through conditional conformal prediction. Intell. Data Anal. 19(6): 1355-1375 (2015) - [j12]Jing Zhao, Aron Henriksson, Lars Asker, Henrik Boström:
Predictive modeling of structured electronic health records for adverse drug event detection. BMC Medical Informatics Decis. Mak. 15-S(4): S1 (2015) - [c70]Jing Zhao, Aron Henriksson, Maria Kvist, Lars Asker, Henrik Boström:
Handling Temporality of Clinical Events for Drug Safety Surveillance. AMIA 2015 - [c69]Aron Henriksson, Jing Zhao, Henrik Boström, Hercules Dalianis:
Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection. BIBM 2015: 343-350 - [c68]Aron Henriksson, Jing Zhao, Henrik Boström, Hercules Dalianis:
Modeling heterogeneous clinical sequence data in semantic space for adverse drug event detection. DSAA 2015: 1-8 - [c67]Jing Zhao, Aron Henriksson, Henrik Boström:
Cascading adverse drug event detection in electronic health records. DSAA 2015: 1-8 - [c66]Andreas Henelius, Kai Puolamäki, Isak Karlsson, Jing Zhao, Lars Asker, Henrik Boström, Panagiotis Papapetrou:
GoldenEye++: A Closer Look into the Black Box. SLDS 2015: 96-105 - [c65]Isak Karlsson, Panagiotis Papapetrou, Henrik Boström:
Forests of Randomized Shapelet Trees. SLDS 2015: 126-136 - [c64]Lars Carlsson, Ernst Ahlberg, Henrik Boström, Ulf Johansson, Henrik Linusson:
Modifications to p-Values of Conformal Predictors. SLDS 2015: 251-259 - [c63]Ulf Johansson, Ernst Ahlberg, Henrik Boström, Lars Carlsson, Henrik Linusson, Cecilia Sönströd:
Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors. SLDS 2015: 271-280 - 2014
- [j11]Andreas Henelius, Kai Puolamäki, Henrik Boström, Lars Asker, Panagiotis Papapetrou:
A peek into the black box: exploring classifiers by randomization. Data Min. Knowl. Discov. 28(5-6): 1503-1529 (2014) - [j10]Catarina Dudas, Amos H. C. Ng, Leif Pehrsson, Henrik Boström:
Integration of data mining and multi-objective optimisation for decision support in production systems development. Int. J. Comput. Integr. Manuf. 27(9): 824-839 (2014) - [j9]Ulf Johansson, Henrik Boström, Tuve Löfström, Henrik Linusson:
Regression conformal prediction with random forests. Mach. Learn. 97(1-2): 155-176 (2014) - [c62]Jing Zhao, Aron Henriksson, Lars Asker, Henrik Boström:
Detecting adverse drug events with multiple representations of clinical measurements. BIBM 2014: 536-543 - [c61]Ulf Johansson, Cecilia Sönströd, Henrik Linusson, Henrik Boström:
Regression trees for streaming data with local performance guarantees. IEEE BigData 2014: 461-470 - [c60]Isak Karlsson, Henrik Boström:
Handling Sparsity with Random Forests When Predicting Adverse Drug Events from Electronic Health Records. ICHI 2014: 17-22 - [c59]Jing Zhao, Aron Henriksson, Henrik Boström:
Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes. ICHI 2014: 285-293 - [c58]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers. AIAI Workshops 2014: 261-270 - [c57]Ulf Johansson, Rikard König, Henrik Linusson, Tuve Löfström, Henrik Boström:
Rule Extraction with Guaranteed Fidelity. AIAI Workshops 2014: 281-290 - [c56]Karl Jansson, Håkan Sundell, Henrik Boström:
gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles. IPDPS Workshops 2014: 1612-1621 - [c55]Lars Asker, Henrik Boström, Isak Karlsson, Panagiotis Papapetrou, Jing Zhao:
Mining candidates for adverse drug interactions in electronic patient records. PETRA 2014: 22:1-22:4 - 2013
- [j8]Thashmee Karunaratne, Henrik Boström, Ulf Norinder:
Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling. Intell. Data Anal. 17(2): 327-341 (2013) - [c54]Orlando P. Zacarias, Henrik Boström:
Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias. ADMA (2) 2013: 189-200 - [c53]Isak Karlsson, Jing Zhao, Lars Asker, Henrik Boström:
Predicting Adverse Drug Events by Analyzing Electronic Patient Records. AIME 2013: 125-129 - [c52]Ulf Johansson, Rikard König, Tuve Löfström, Henrik Boström:
Evolved decision trees as conformal predictors. IEEE Congress on Evolutionary Computation 2013: 1794-1801 - [c51]Ulf Johansson, Tuve Löfström, Henrik Boström:
Overproduce-and-select: The grim reality. CIEL 2013: 52-59 - [c50]Ulf Johansson, Henrik Boström, Tuve Löfström:
Conformal Prediction Using Decision Trees. ICDM 2013: 330-339 - [c49]Ulf Johansson, Tuve Löfström, Henrik Boström:
Random brains. IJCNN 2013: 1-8 - [c48]Tuve Löfström, Ulf Johansson, Henrik Boström:
Effective utilization of data in inductive conformal prediction using ensembles of neural networks. IJCNN 2013: 1-8 - [c47]Amos H. C. Ng, Catarina Dudas, Henrik Boström, Kalyanmoy Deb:
Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster Convergence. LION 2013: 1-18 - 2012
- [j7]Ulf Johansson, Cecilia Sönströd, Tuve Löfström, Henrik Boström:
Obtaining accurate and comprehensible classifiers using oracle coaching. Intell. Data Anal. 16(2): 247-263 (2012) - [j6]Henrik Boström:
Forests of Probability Estimation Trees. Int. J. Pattern Recognit. Artif. Intell. 26(2) (2012) - [j5]Ulf Norinder, Henrik Boström:
Introducing Uncertainty in Predictive Modeling - Friend or Foe? J. Chem. Inf. Model. 52(11): 2815-2822 (2012) - [c46]Sampath Deegalla, Henrik Boström, Keerthi Walgama:
Choice of dimensionality reduction methods for feature and classifier fusion with nearest neighbor classifiers. FUSION 2012: 875-881 - [c45]Thashmee Karunaratne, Henrik Boström:
Can Frequent Itemset Mining Be Efficiently and Effectively Used for Learning from Graph Data? ICMLA (1) 2012: 409-414 - [c44]Constantino Sotomane, Jordi Gallego-Ayala, Lars Asker, Henrik Boström, Venâncio Massingue:
Extracting Patterns from Socioeconomic Databases to Characterize Small Farmers with High and Low Corn Yields in Mozambique: a Data Mining Approach. ICDM (Workshops) 2012: 99-108 - 2011
- [c43]Henrik Boström:
Concurrent Learning of Large-Scale Random Forests. SCAI 2011: 20-29 - 2010
- [c42]Thashmee Karunaratne, Henrik Boström, Ulf Norinder:
Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - A Case Study with Medicinal Chemistry Datasets. ICMLA 2010: 828-833 - [c41]Tuve Löfström, Ulf Johansson, Henrik Boström:
Comparing methods for generating diverse ensembles of artificial neural networks. IJCNN 2010: 1-6 - [c40]Cecilia Sönströd, Ulf Johansson, Henrik Boström, Ulf Norinder:
Pin-pointing concept descriptions. SMC 2010: 2956-2963
2000 – 2009
- 2009
- [c39]Tuve Löfström, Ulf Johansson, Henrik Boström:
Ensemble member selection using multi-objective optimization. CIDM 2009: 245-251 - [c38]Sampath Deegalla, Henrik Boström:
Fusion of dimensionality reduction methods: A case study in microarray classification. FUSION 2009: 460-465 - [c37]Thashmee Karunaratne, Henrik Boström:
Graph Propositionalization for Random Forests. ICMLA 2009: 196-201 - [c36]Sampath Deegalla, Henrik Boström:
Improving Fusion of Dimensionality Reduction Methods for Nearest Neighbor Classification. ICMLA 2009: 771-775 - [c35]Catarina Dudas, Henrik Boström:
Using uncertain chemical and thermal data to predict product quality in a casting process. KDD Workshop on Knowledge Discovery from Uncertain Data 2009: 57-61 - 2008
- [c34]Ulf Johansson, Henrik Boström, Rikard König:
Extending Nearest Neighbor Classification with Spheres of Confidence. FLAIRS 2008: 282-287 - [c33]Henrik Boström, Ronnie Johansson, Alexander Karlsson:
On evidential combination rules for ensemble classifiers. FUSION 2008: 1-8 - [c32]Henrik Boström:
Calibrating Random Forests. ICMLA 2008: 121-126 - [c31]Tuve Löfström, Ulf Johansson, Henrik Boström:
On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers. ICMLA 2008: 127-132 - [c30]Cecilia Sönströd, Ulf Johansson, Ulf Norinder, Henrik Boström:
Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes. ICMLA 2008: 559-564 - [c29]Ulf Johansson, Tuve Löfström, Henrik Boström:
The problem with ranking ensembles based on training or validation performance. IJCNN 2008: 3222-3228 - [c28]Ronnie Johansson, Henrik Boström, Alexander Karlsson:
A study on class-specifically discounted belief for ensemble classifiers. MFI 2008: 614-619 - [c27]Ulf Johansson, Cecilia Sönströd, Tuve Löfström, Henrik Boström:
Chipper - A Novel Algorithm for Concept Description. SCAI 2008: 133-140 - 2007
- [c26]Henrik Boström:
Feature vs. classifier fusion for predictive data mining a case study in pesticide classification. FUSION 2007: 1-7 - [c25]Henrik Boström:
Estimating class probabilities in random forests. ICMLA 2007: 211-216 - [c24]Sampath Deegalla, Henrik Boström:
Classification of Microarrays with kNN: Comparison of Dimensionality Reduction Methods. IDEAL 2007: 800-809 - [c23]Thashmee Karunaratne, Henrik Boström:
Using Background Knowledge for Graph Based Learning: A Case Study in Chemoinformatics. IMECS 2007: 153-157 - [c22]Henrik Boström:
Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets. SDM 2007: 27-34 - 2006
- [c21]Thashmee Karunaratne, Henrik Boström:
Learning to classify structured data by graph propositionalization. Computational Intelligence 2006: 283-288 - [c20]Sampath Deegalla, Henrik Boström:
Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification. ICMLA 2006: 245-250 - 2004
- [j4]Tony Lindgren, Henrik Boström:
Resolving rule conflicts with double induction. Intell. Data Anal. 8(5): 457-468 (2004) - 2003
- [c19]Tony Lindgren, Henrik Boström:
Resolving Rule Conflicts with Double Induction. IDA 2003: 60-67 - 2002
- [c18]Tony Lindgren, Henrik Boström:
Classification with Intersecting Rules. ALT 2002: 395-402 - [c17]Per Lidén, Lars Asker, Henrik Boström:
Rule Induction for Classification of Gene Expression Array Data. PKDD 2002: 338-347 - 2001
- [j3]Juan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström:
Boosting interval based literals. Intell. Data Anal. 5(3): 245-262 (2001) - [c16]Anette Hulth, Jussi Karlgren, Anna Jonsson, Henrik Boström, Lars Asker:
Automatic Keyword Extraction Using Domain Knowledge. CICLing 2001: 472-482 - [c15]Martin Eineborg, Henrik Boström:
Classifying Uncovered Examples by Rule Stretching. ILP 2001: 41-50 - [c14]Mikael Huss, Henrik Boström, Lars Asker, Joakim Cöster:
Learning to recognize brain specific proteins based on low-level features from on-line prediction servers. BIOKDD 2001: 45-49 - 2000
- [c13]Juan José Rodríguez, Carlos J. Alonso, Henrik Boström:
Learning First Order Logic Time Series Classifiers. ILP Work-in-progress reports 2000 - [c12]Juan J. Rodríguez Diez, Carlos Alonso González, Henrik Boström:
Learning First Order Logic Time Series Classifiers: Rules and Boosting. PKDD 2000: 299-308
1990 – 1999
- 1999
- [j2]Henrik Boström, Peter Idestam-Almquist:
Induction of Logic Programs by Example-Guided Unfolding. J. Log. Program. 40(2-3): 159-183 (1999) - [c11]Henrik Boström, Lars Asker:
Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction. ILP 1999: 33-43 - [c10]Henrik Boström:
Induction of Recursive Transfer Rules. Learning Language in Logic 1999: 237-246 - 1998
- [c9]Henrik Boström:
Predicate Invention and Learning from Positive Examples Only. ECML 1998: 226-237 - 1997
- [j1]Zoltán Alexin, Tibor Gyimóthy, Henrik Boström:
IMPUT: An Interactive Learning Tool Based on Program Specialization. Intell. Data Anal. 1(1-4): 219-244 (1997) - 1996
- [c8]Zoltán Alexin, Tibor Gyimóthy, Henrik Boström:
Integrating Algorithmic Debugging and Unfolding Transformation in an Interactive Learner. ECAI 1996: 403-407 - [c7]Henrik Boström:
Theory-Guideed Induction of Logic Programs by Inference of Regular Languages. ICML 1996: 46-53 - 1995
- [c6]Henrik Boström:
Specialization of Recursive Predicates. ECML 1995: 92-106 - [c5]Hilde Adé, Henrik Boström:
JIGSAW: Puzzling together RUTH and SPECTRE (Extended Abstract). ECML 1995: 263-266 - [c4]Henrik Boström:
Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs. IJCAI 1995: 1194-1200 - 1993
- [c3]Henrik Boström:
Improving Example-Guided Unfolding. ECML 1993: 124-135 - 1991
- [c2]Carl Gustaf Jansson, Henrik Boström, Peter Idestam-Almquist:
Optimizing Horn Clause Logic Programs for Particular Modes of Use: An Analysis of Explanation-Based Learning and Partial Evaluation. SCAI 1991: 252-257 - 1990
- [c1]Henrik Boström:
Generalizing the Order of Goals as an Approach to Generalizing Number. ML 1990: 260-267
Coauthor Index
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