Search results for "Problem transformation"

showing 2 items of 2 documents

Multi-label Methods for Prediction with Sequential Data

2017

The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data. If labels indices are considered as time indices, the problems can often be seen as equivalent. In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data. From this study we draw upon the most suitable techniques from the area and develop two novel competitive approaches which can be applied to either kind of data. We carry out an empirical evaluation inves…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceMarkov modelsMulti-label classificationMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreMarkov modelMachine learningTask (project management)Machine Learning (cs.LG)Statistics - Machine LearningArtificial Intelligence020204 information systemsComputer Science - Data Structures and Algorithms0202 electrical engineering electronic engineering information engineeringSequential dataData Structures and Algorithms (cs.DS)Multi-label classificationta113business.industryProblem transformationSignal ProcessingSequence prediction020201 artificial intelligence & image processingSequential dataComputer Vision and Pattern RecognitionData miningArtificial intelligencebusinesscomputerSoftware
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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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