Feature Selection Algorithm Based on Association Rules

Qu, Yi; Fang, Yu; Yan, Fengqi

doi:10.1088/1742-6596/1168/5/052012

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…The key idea of assessing the redundancy of expression items is to find closely related ones. 29 , 40 Results were also expressed in terms of odds ratio, which was calculated using the additional “fmsb” R package. Anonymized data can be made available to qualified investigators on request to the corresponding author.…”

Section: Methodsmentioning

confidence: 99%

Sorting pain out of salience: assessment of pain facial expressions in the human fetus

Bernardes

Harnik

Teixeira

et al. 2021

PR9

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Introduction:The question of whether the human fetus experiences pain has received substantial attention in recent times. With the advent of high-definition 4-dimensional ultrasound (4D-US), it is possible to record fetal body and facial expressions. Objective: To determine whether human fetuses demonstrate discriminative acute behavioral responses to nociceptive input. Methods: This cross-sectional study included 5 fetuses with diaphragmatic hernia with indication of intrauterine surgery (fetoscopic endoluminal tracheal occlusion) and 8 healthy fetuses, who were scanned with 4D-US in 1 of 3 conditions: (1) acute pain group: Fetuses undergoing intrauterine surgery were assessed in the preoperative period during the anesthetic injection into the thigh; (2) control group at rest: Facial expressions at rest were recorded during scheduled ultrasound examinations; and (3) control group acoustic startle: Fetal facial expressions were recorded during acoustic stimulus (500-4000 Hz; 60-115 dB). Results: Raters blinded to the fetuses' groups scored 65 pictures of fetal facial expressions based on the presence of 12 items (facial movements). Analyses of redundancy and usefulness excluded 5 items for being of low discrimination capacity (P.0.2). The final version of the pain assessment tool consisted of a total of 7 items: brow lowering/eyes squeezed shut/deepening of the nasolabial furrow/open lips/horizontal mouth stretch/vertical mouth stretch/neck deflection. Odd ratios for a facial expression to be detected in acute pain compared with control conditions ranged from 11 (neck deflection) to 1,400 (horizontal mouth stretch). Using the seven-item final tool, we showed that 5 is the cutoff value discriminating pain from nonpainful startle and rest. Conclusions: This study inaugurates the possibility to study pain responses during the intrauterine life, which may have implications for the postoperative management of pain after intrauterine surgical interventions

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Section: Methodsmentioning

confidence: 99%

Sorting pain out of salience: assessment of pain facial expressions in the human fetus

Bernardes

Harnik

Teixeira

et al. 2021

PR9

View full text Add to dashboard Cite

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“…The feature selection method based on association rules, ARFS, was developed in Reference 16. The algorithm used association rules in order to extract the frequent 2‐items set of the category and feature attributes in the dataset.…”

Section: Related Workmentioning

confidence: 99%

A new feature selection method based on frequent and associated itemsets for text classification

Farghaly

El‐Hafeez

2022

Concurrency and Computation

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SummaryFeature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low‐quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this article focuses on the association analysis theory in data mining to select important features. In this study, a novel feature selection method based on frequent and associated itemsets (FS‐FAI) for text classification is proposed. FS‐FAI seeks to find relevant features and also takes feature interaction into account. Moreover, it uses association as a metric to evaluate the relativity between the target concept and feature(s). To evaluate the efficacy of the proposed method, several experiments were conducted on a BBC dataset from the BBC news website and SMS spam collection dataset from the UCI machine learning repository. The obtained results were compared to well‐known feature selection methods. The reported results demonstrated the effectiveness of the proposed feature selection method in selecting high‐quality features and in handling redundant information in text classification.

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“…The feature selection method based on association rules, ARFS, was developed in [11]. The algorithm used association rules in order to extract the frequent 2items set of the category and feature attributes in the data set.…”

Section: Related Workmentioning

confidence: 99%

A New Feature Selection Method Based on Frequent and Associated Itemsets For Text Classification

Farghaly

El‐Hafeez

2022

Preprint

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Feature selection is one of the major issues in pattern recognition. The quality of selected features is important for classification as the low-quality data can degrade the model construction performance. Due to the difficulty of dealing with the problem that selected features always contain redundant information, this paper focuses on the association analysis theory in data mining to select important features. In this study, a novel Feature Selection method based on Frequent and Associated Itemsets (FS-FAI) for text classification is proposed. FS-FAI seeks to find relevant features and also takes feature interaction into account. Moreover, it uses association as a metric to evaluate the relativity between the target concept and feature(s). To evaluate the efficacy of the proposed method, several experiments were conducted on a BBC data set from the BBC news website. The obtained results were compared to well-known feature selection methods. The reported results demonstrated the effectiveness of the proposed feature selection method in selecting high-quality features and in handling redundant information in text classification.

show abstract

Feature Selection Algorithm Based on Association Rules

Cited by 14 publications

References 6 publications

Sorting pain out of salience: assessment of pain facial expressions in the human fetus

Sorting pain out of salience: assessment of pain facial expressions in the human fetus

A new feature selection method based on frequent and associated itemsets for text classification

A New Feature Selection Method Based on Frequent and Associated Itemsets For Text Classification

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