Search results for "EMLM"

showing 2 items of 2 documents

Feature selection for distance-based regression: An umbrella review and a one-shot wrapper

2023

Feature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for the recently introduced distance-based supervised machine learning model is considered for regression problems. The study is contextualized by first providing an umbrella review (review of reviews) of recent development in the research field. We then propose a saliency-based one-shot wrapper algorithm for FS, which is called MAS-FS. The algorithm is compared with a set of other popular FS algorithms, using a versatile set of simulated and benchmark datasets. Finally, experimental results underline the usefulness of FS for regression, confirm…

EMLMfeature selectionkoneoppiminenArtificial IntelligenceCognitive Neurosciencealgoritmitparantaminen (paremmaksi muuttaminen)tekoälydistance-based methodwrapper algorithmfeature saliencyComputer Science ApplicationsNeurocomputing
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Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression

2020

Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM. peerReviewed

the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM)koneoppiminenemphasizing the utility of the problem transformation especially with the EMLM.Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methodsin problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential
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