2016
DOI: 10.1111/exsy.12173
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Unsupervised feature selection for interpretable classification in behavioral assessment of children

Abstract: In this paper, we consider a data set taken from the administration of the Behavior Assessment\ud System for Children test to 157 subjects, and we approach the problem of clustering and classify\ud the subjects in an interpretable fashion. Because the Behavior Assessment System for Children test\ud is originally composed of 149 questions (152 in the particular version used for this experiment), we\ud first propose a feature selection wrapper model composed by a multi‐objective evolutionary\ud algorithm, the it… Show more

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Cited by 12 publications
(7 citation statements)
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References 48 publications
(77 reference statements)
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“…Another wrapper method is proposed in Shi et al, 57 while in García-Nieto et al, 58 2 wrapper methods with 3 and 2 objectives, respectively, applied to cancer diagnosis are compared. Finally, very recent examples of multiobjective FS systems can be found in Jiménez et al 59,60 To the best of our knowledge, the only attempt to use multiobjective optimization process in modeling air quality has been used for monitoring system planning in Sarigiannis and Saisana. 61…”
Section: Introductionmentioning
confidence: 99%
“…Another wrapper method is proposed in Shi et al, 57 while in García-Nieto et al, 58 2 wrapper methods with 3 and 2 objectives, respectively, applied to cancer diagnosis are compared. Finally, very recent examples of multiobjective FS systems can be found in Jiménez et al 59,60 To the best of our knowledge, the only attempt to use multiobjective optimization process in modeling air quality has been used for monitoring system planning in Sarigiannis and Saisana. 61…”
Section: Introductionmentioning
confidence: 99%
“…Another domain that has recently been subjected to highly innovative research practices is health, with distinct examples of applications such as nutrition (Espín, Hurtado, & Noguera, 2016), patient care (James, Calderon, & Cook, 2017), depression (Chattopadhyay, 2014), and cancer (Acharya, Ng, Sree, Chua, & Chattopadhyay, 2014). Psychology and social sciences have also benefited from ESs' implementations (e.g., Jiménez, Jódar, Martín, Sánchez, & Sciavicco, 2016). Such diversity shows the broad applicability of ESs, providing solid grounded evidence of its relevance.…”
Section: Introductionmentioning
confidence: 99%
“…We decided to solve this problem by applying multi-objective evolutionary algorithms ( MOEA ) [ 15 , 16 ] as meta-heuristics, and, in particular, two known algorithms: NSGA-II [ 15 ] and ENORA [ 17 ]. They are both state-of-the-art evolutionary algorithms which have been applied, and compared, on several occasions [ 18 , 19 , 20 ]. NSGA-II is very well-known and has the advantage of being available in many implementations, while ENORA generally has a higher performance.…”
Section: Introductionmentioning
confidence: 99%