A Minority Class Feature Selection Method

Cuaya, German; Muñoz-Meléndez, Angélica; Morales, Eduardo F.

doi:10.1007/978-3-642-25085-9_49

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Five of the feature selection methods were used from Weka [33] corresponding to the most representative of the classification above, and they were ReliefFAtributeEval, OneRAttributeEval, SymetricalUncertAttributeEval, Wrap-perSubsetEval, CorrelationAttributeEval, and a homemade algorithm called Feature Selection for Minority Class (FSMC) [34].…”

Section: Feature Selectionmentioning

confidence: 99%

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Cuaya-Simbro

Sanpablo

Morales

et al. 2021

Journal of Healthcare Engineering

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Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).

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Section: Feature Selectionmentioning

confidence: 99%

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Cuaya-Simbro

Sanpablo

Morales

et al. 2021

Journal of Healthcare Engineering

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“…The FSMC is determining the difference between feature values for majority and minority class. It sorts the features by the highest difference between the values for majority and minority class [35]. Third, a mixed wrapper method was implemented (Fig.…”

Section: Pnnx [%]mentioning

confidence: 99%

Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring

Werth

Long

Zwartkruis-Pelgrim

et al. 2017

Early Human Development

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“…FilteredSubsetEval provides two meta attribute selection evaluators that can apply an arbitrary filter to the input data before executing the actual attribute selection scheme. The methods are selected for their comparable performance as stated in [31].…”

Section: B Comparing Feature Selection Methodsmentioning

confidence: 99%

Feature selection for Malaysian medicinal plant leaf shape identification and classification

Sainin

Alfred

2014

2014 International Conference on Computational Science and Technology (ICCST)

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Malaysian medicinal plants may be abundant natural resources but there has not been much research done on preserving the knowledge of these medicinal plants which enables general public to know the leaf using computing capability. Therefore, in this preliminary study, a novel framework in order to identify and classify tropical medicinal plants in Malaysia based on the extracted patterns from the leaf is presented. The extracted patterns from medicinal plant leaf are obtained based on several angle features. However, the extracted features create quite large number of attributes (features), thus degrade the performance most of the classifiers. Thus, a feature selection is applied to leaf data and to investigate whether the performance of a classifier can be improved. Wrapper based genetic algorithm (GA) feature selection is used to select the features and the ensemble classifier called Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) is used as a classifier. The performance of the feature selection is compared with two feature selections from Weka. In the experiment, five species of Malaysian medicinal plants are identified and classified in which will be represented by using 65 images. This study is important in order to assist local community to utilize the knowledge and application of Malaysian medicinal plants for future generation.

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A Minority Class Feature Selection Method

Cited by 10 publications

References 11 publications

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring

Feature selection for Malaysian medicinal plant leaf shape identification and classification

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