FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385540556> ?p ?o ?g. }

• W4385540556 endingPage "116604" @default.
• W4385540556 startingPage "116604" @default.
• W4385540556 abstract "In the digital soil mapping framework, machine learning (ML) algorithms are currently the most popular methods for the spatial prediction of soil properties. The fast developments of easy-to-use software implementations for a large panel of ML algorithms have encouraged comparison studies between algorithms, with the goal of ranking their performances and identifying the best ones among them. However, as no firm conclusions can be drawn about the best ML algorithm to be used in general, this suggests that combining a set of them could be a better approach. Numerous methods have been proposed to do so, most of them relying on a linear weighting of the individual algorithms. However, there are almost as many methods for linearly weighting ML algorithms as there are ML algorithms, thus leaving the problem unsolved. Moreover, these weighting methods are mostly used out-of-the-box, without paying a proper attention to the associated hypotheses. In this paper, we propose to address this issue by setting the problem in a more formal framework. Starting from classical hypotheses, it is shown how the benefit of averaging various ML algorithms can be estimated from their joint performances. Relying afterwards on the most commonly used linear weighting schemes, it is reminded that, as long as the performance metrics are based on mean square errors, the best averaging method is by essence the best linear (unbiased) predictor. Using a more general Bayesian framework, it is also shown that accounting for conditional biases when weighting ML algorithms is a key issue for obtaining improved predictions, and explicit formulas are proposed for that goal. Finally, these theoretical results are illustrated and discussed using a soil data set collected over an arid and semi-arid region in Iran where clay content, calcium carbonate equivalent, soil organic carbon and electrical conductivity were measured in topsoil samples." @default.
• W4385540556 created "2023-08-04" @default.
• W4385540556 creator A5056756521 @default.
• W4385540556 creator A5069268571 @default.
• W4385540556 creator A5076408429 @default.
• W4385540556 date "2023-09-01" @default.
• W4385540556 modified "2023-09-23" @default.
• W4385540556 title "Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework" @default.
• W4385540556 cites W1964825543 @default.
• W4385540556 cites W1964940342 @default.
• W4385540556 cites W1984828485 @default.
• W4385540556 cites W1986528915 @default.
• W4385540556 cites W1994672023 @default.
• W4385540556 cites W1994894661 @default.
• W4385540556 cites W2056435747 @default.
• W4385540556 cites W2120037005 @default.
• W4385540556 cites W2139709933 @default.
• W4385540556 cites W2161020850 @default.
• W4385540556 cites W2165895388 @default.
• W4385540556 cites W2186294614 @default.
• W4385540556 cites W2313339984 @default.
• W4385540556 cites W2404286957 @default.
• W4385540556 cites W2410552594 @default.
• W4385540556 cites W2597412011 @default.
• W4385540556 cites W2598382903 @default.
• W4385540556 cites W2815885864 @default.
• W4385540556 cites W2889701190 @default.
• W4385540556 cites W2890179699 @default.
• W4385540556 cites W2908031888 @default.
• W4385540556 cites W2911964244 @default.
• W4385540556 cites W2931266020 @default.
• W4385540556 cites W2987708268 @default.
• W4385540556 cites W2996717911 @default.
• W4385540556 cites W3005528129 @default.
• W4385540556 cites W3086056576 @default.
• W4385540556 cites W3096190728 @default.
• W4385540556 cites W3102476541 @default.
• W4385540556 cites W3112145214 @default.
• W4385540556 cites W3122338430 @default.
• W4385540556 cites W3124733172 @default.
• W4385540556 cites W3136376581 @default.
• W4385540556 cites W3153963714 @default.
• W4385540556 cites W3177036350 @default.
• W4385540556 cites W3199918518 @default.
• W4385540556 cites W3211651883 @default.
• W4385540556 cites W3215367274 @default.
• W4385540556 cites W4205924964 @default.
• W4385540556 cites W4206345561 @default.
• W4385540556 cites W4210284390 @default.
• W4385540556 cites W4214672600 @default.
• W4385540556 doi "https://doi.org/10.1016/j.geoderma.2023.116604" @default.
• W4385540556 hasPublicationYear "2023" @default.
• W4385540556 type Work @default.
• W4385540556 citedByCount "0" @default.
• W4385540556 crossrefType "journal-article" @default.
• W4385540556 hasAuthorship W4385540556A5056756521 @default.
• W4385540556 hasAuthorship W4385540556A5069268571 @default.
• W4385540556 hasAuthorship W4385540556A5076408429 @default.
• W4385540556 hasBestOaLocation W43855405561 @default.
• W4385540556 hasConcept C107673813 @default.
• W4385540556 hasConcept C11413529 @default.
• W4385540556 hasConcept C119857082 @default.
• W4385540556 hasConcept C121955636 @default.
• W4385540556 hasConcept C124101348 @default.
• W4385540556 hasConcept C126838900 @default.
• W4385540556 hasConcept C144133560 @default.
• W4385540556 hasConcept C154945302 @default.
• W4385540556 hasConcept C177264268 @default.
• W4385540556 hasConcept C183115368 @default.
• W4385540556 hasConcept C189430467 @default.
• W4385540556 hasConcept C196083921 @default.
• W4385540556 hasConcept C199360897 @default.
• W4385540556 hasConcept C26517878 @default.
• W4385540556 hasConcept C38652104 @default.
• W4385540556 hasConcept C41008148 @default.
• W4385540556 hasConcept C71924100 @default.
• W4385540556 hasConceptScore W4385540556C107673813 @default.
• W4385540556 hasConceptScore W4385540556C11413529 @default.
• W4385540556 hasConceptScore W4385540556C119857082 @default.
• W4385540556 hasConceptScore W4385540556C121955636 @default.
• W4385540556 hasConceptScore W4385540556C124101348 @default.
• W4385540556 hasConceptScore W4385540556C126838900 @default.
• W4385540556 hasConceptScore W4385540556C144133560 @default.
• W4385540556 hasConceptScore W4385540556C154945302 @default.
• W4385540556 hasConceptScore W4385540556C177264268 @default.
• W4385540556 hasConceptScore W4385540556C183115368 @default.
• W4385540556 hasConceptScore W4385540556C189430467 @default.
• W4385540556 hasConceptScore W4385540556C196083921 @default.
• W4385540556 hasConceptScore W4385540556C199360897 @default.
• W4385540556 hasConceptScore W4385540556C26517878 @default.
• W4385540556 hasConceptScore W4385540556C38652104 @default.
• W4385540556 hasConceptScore W4385540556C41008148 @default.
• W4385540556 hasConceptScore W4385540556C71924100 @default.
• W4385540556 hasLocation W43855405561 @default.
• W4385540556 hasOpenAccess W4385540556 @default.
• W4385540556 hasPrimaryLocation W43855405561 @default.
• W4385540556 hasRelatedWork W1987481527 @default.
• W4385540556 hasRelatedWork W2027702703 @default.

• next