Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning

We design several algorithms representing evaluation processes of different complexity, ranging from basic environments based on a predetermined number of features to complex structures involving alternatives defined through decision trees whose number of nodes is determined by the cardinality of th...

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Autores principales:	Debora Di Caprio, Francisco J. Santos-Arteaga
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Feature positioning Information retrieval Pattern recognition Machine learning Decision trees Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/befbd61f2c784f028f2de8f0c3ebb23d
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spelling	oai:doaj.org-article:befbd61f2c784f028f2de8f0c3ebb23d2021-11-14T04:36:00ZEnhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning2666-827010.1016/j.mlwa.2021.100196https://doaj.org/article/befbd61f2c784f028f2de8f0c3ebb23d2022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021000980https://doaj.org/toc/2666-8270We design several algorithms representing evaluation processes of different complexity, ranging from basic environments based on a predetermined number of features to complex structures involving alternatives defined through decision trees whose number of nodes is determined by the cardinality of the respective power sets. The sequential structure of these evaluation processes builds on the information retrieval behavior of users in online search environments. The algorithms generate two strings of data, namely, numerical evaluations determining the retrieval behavior of users and the subsequent choices made by the latter. The way the output obtained from the algorithms is inputted within the vectors summarizing the complexity of the evaluation processes conditions the capacity of machine learning techniques to categorize them correctly. The main purpose of the research is to illustrate numerically two main results. First, machine learning techniques categorize processes correctly even if their characteristic features are presented in a way that prevents their identification using standard statistical techniques. Second, the accuracy of the categorization capacities of these techniques can be substantially enhanced by describing the retrieval processes in the way required to implement standard statistical analyses. We perform a battery of tests using machine learning techniques to demonstrate and analyze these results. Their applicability to classification and prediction problems in medical environments, particularly those constrained by the quality of the data available, is emphasized.Debora Di CaprioFrancisco J. Santos-ArteagaElsevierarticleFeature positioningInformation retrievalPattern recognitionMachine learningDecision treesCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100196- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Feature positioning Information retrieval Pattern recognition Machine learning Decision trees Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Feature positioning Information retrieval Pattern recognition Machine learning Decision trees Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Debora Di Caprio Francisco J. Santos-Arteaga Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
description	We design several algorithms representing evaluation processes of different complexity, ranging from basic environments based on a predetermined number of features to complex structures involving alternatives defined through decision trees whose number of nodes is determined by the cardinality of the respective power sets. The sequential structure of these evaluation processes builds on the information retrieval behavior of users in online search environments. The algorithms generate two strings of data, namely, numerical evaluations determining the retrieval behavior of users and the subsequent choices made by the latter. The way the output obtained from the algorithms is inputted within the vectors summarizing the complexity of the evaluation processes conditions the capacity of machine learning techniques to categorize them correctly. The main purpose of the research is to illustrate numerically two main results. First, machine learning techniques categorize processes correctly even if their characteristic features are presented in a way that prevents their identification using standard statistical techniques. Second, the accuracy of the categorization capacities of these techniques can be substantially enhanced by describing the retrieval processes in the way required to implement standard statistical analyses. We perform a battery of tests using machine learning techniques to demonstrate and analyze these results. Their applicability to classification and prediction problems in medical environments, particularly those constrained by the quality of the data available, is emphasized.
format	article
author	Debora Di Caprio Francisco J. Santos-Arteaga
author_facet	Debora Di Caprio Francisco J. Santos-Arteaga
author_sort	Debora Di Caprio
title	Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
title_short	Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
title_full	Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
title_fullStr	Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
title_full_unstemmed	Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning
title_sort	enhancing the pattern recognition capacity of machine learning techniques: the importance of feature positioning
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/befbd61f2c784f028f2de8f0c3ebb23d
work_keys_str_mv	AT deboradicaprio enhancingthepatternrecognitioncapacityofmachinelearningtechniquestheimportanceoffeaturepositioning AT franciscojsantosarteaga enhancingthepatternrecognitioncapacityofmachinelearningtechniquestheimportanceoffeaturepositioning
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Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning

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