H
Henrik Boström
Researcher at Royal Institute of Technology
Publications - 176
Citations - 2526
Henrik Boström is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Random forest & Decision tree. The author has an hindex of 24, co-authored 163 publications receiving 2137 citations. Previous affiliations of Henrik Boström include Stockholm University & University of Skövde.
Papers
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On the Definition of Information Fusion as a Field of Research
Henrik Boström,Sten F. Andler,Marcus Brohede,Ronnie Johansson,Alexander Karlsson,Joeri van Laere,Lars Niklasson,Marie Nilsson,Anne Persson,Tom Ziemke +9 more
TL;DR: A more precise definition of the field of information fusion can be of benefit to researchers within the field, who may use a definition when motivating their own work and evaluating the results of other researchers' work.
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Generalized random shapelet forests
TL;DR: A novel tree-based ensemble method for univariate and multivariate time series classification using shapelets, called the generalized random shapelet forest algorithm, which yields predictive performance comparable to the current state-of-the-art and significantly outperforms several alternative algorithms, while being at least an order of magnitude faster.
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Improving structure-based virtual screening by multivariate analysis of scoring data.
TL;DR: A new two-stage approach is suggested for structure-based virtual screening where limited activity information is available and the classifiers show a superior performance, with rule-based methods being most effective.
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Regression conformal prediction with random forests
TL;DR: In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors.
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A peek into the black box: exploring classifiers by randomization
TL;DR: An efficient iterative algorithm to find the attributes and dependencies used by any classifier when making predictions is proposed and the empirical investigation shows that the novel algorithm is indeed able to find groupings of interacting attributes exploited by the different classifiers.